forth commit

CHANNEL_FILTERING_README.md

@@ -0,0 +1,82 @@
+# 通道级滤波重构说明
+
+## 问题描述
+
+原来的滤波逻辑存在严重问题：
+- **错误做法**：对单个数据包内的短时间序列进行滤波
+- **问题**：滤波器无法获得足够的上下文信息，导致滤波效果差
+- **后果**：信号质量下降，可能引入伪影
+
+## 解决方案
+
+新的通道级滤波逻辑：
+1. **收集数据**：收集所有数据包中相同通道的完整数据
+2. **整体滤波**：对完整通道数据进行滤波处理
+3. **重新分配**：将滤波后的数据重新分配回数据包结构
+
+## 核心方法
+
+### `process_channel_based_filtering()`
+- 输入：多个数据包的向量
+- 输出：处理后的数据包向量
+- 功能：实现通道级滤波的核心逻辑
+
+### `apply_channel_filters()`
+- 根据设备类型选择合适的滤波策略
+- 支持EEG、ECG、PPG、呼吸、打鼾、听诊器等设备
+
+## 使用方法
+
+### 原来的错误用法
+```cpp
+// ❌ 错误：对单个数据包滤波
+std::vector<SensorData> raw_packets = get_raw_data_packets();
+for (auto& packet : raw_packets) {
+    packet = signal_processor.preprocess_signals(packet); // 错误！
+}
+```
+
+### 新的正确用法
+```cpp
+// ✅ 正确：通道级滤波
+std::vector<SensorData> raw_packets = get_raw_data_packets();
+std::vector<SensorData> processed_packets = 
+    signal_processor.process_channel_based_filtering(raw_packets);
+```
+
+## 滤波流程
+
+```
+原始数据包 → 按通道收集 → 完整通道滤波 → 重新分配 → 处理后数据包
+     ↓              ↓           ↓           ↓           ↓
+   [包1,包2,包3] → [通道1数据] → [滤波后] → [新包1,新包2,新包3]
+                   [通道2数据] → [滤波后]
+                   [通道3数据] → [滤波后]
+```
+
+## 优势
+
+1. **滤波质量提升**：滤波器获得完整的时间序列信息
+2. **边界效应减少**：避免数据包边界处的滤波伪影
+3. **计算效率**：批量处理，减少重复计算
+4. **信号连续性**：保持通道数据的连续性
+
+## 注意事项
+
+1. **内存使用**：需要临时存储完整通道数据
+2. **数据包重构**：滤波后需要重新构建数据包结构
+3. **时序保持**：确保数据包的时间顺序正确
+
+## 测试
+
+运行测试文件验证功能：
+```bash
+g++ -o test_channel_filtering test_channel_filtering.cpp src/signal_processor/signal_processor.cpp -I./include
+./test_channel_filtering
+```
+
+## 兼容性
+
+- 保持原有API不变
+- 新增方法不影响现有代码
+- 可以逐步迁移到新的滤波逻辑

include/File_manage.h

@@ -4,7 +4,7 @@
 class FileManager {
 public:
     static std::vector<uint8_t> readBinaryFile(const std::string& filename); // 读取二进制文件
-    
+    bool is_mit_bih_file(const std::vector<uint8_t>& data); 
 };
 void save_to_csv(const std::vector<SensorData>& all_data, const std::string& filename);//将数据写入csv
 #endif

include/device_praser.h

@@ -20,6 +20,7 @@ SensorData parse_stethoscope(const uint8_t* data);
 SensorData parse_snore(const uint8_t* data);
 SensorData parse_respiration(const uint8_t* data);
 std::vector<SensorData> parse_device_data(const std::vector<uint8_t>& file_data);
+SensorData parse_mit_bih(const uint8_t* data, size_t size, int num_channels, int samples_per_channel);
 
 
 #endif

include/headfile.h

@@ -1,5 +1,6 @@
 #ifndef _HEADFILE_H
 #define _HEADFILE_H
+
 #include "test.h"
 #include "data_receiver.h"
 #include <iostream>
@@ -24,7 +25,9 @@
 #include <iterator>
 #include "File_manage.h"
 #include <cmath>
+#include "indicator_cal.h"
 #include <algorithm>
 #include "feature_extractor.h"
 #include <utility> // for std::pair
+
 #endif

include/indicator_cal.h

@@ -0,0 +1,18 @@
+#ifndef _INDICATOR_CAL_H
+#define _INDICATOR_CAL_H
+#include "headfile.h"
+class MetricsCalculator
+{
+    public:
+    float calculate_heart_rate_ecg(const SensorData& ecg_signal, float sample_rate);
+    float calculate_t_wave_amplitude(const std::vector<float>& ecg_signal);
+    float calculate_heart_rate_ppg(const std::vector<float>& ppg_signal, float sample_rate);
+    float calculate_spo2(const SensorData& ppg_data);
+    float calculate_pulse_width(const std::vector<float>& ppg_signal);
+    float calculate_amplitude_ratio(const SensorData& ppg_data);
+    float calculate_sdnn(const std::vector<float>& rr_intervals);
+    float calculate_rmssd(const std::vector<float>& rr_intervals) ;
+    std::vector<float> detect_r_peaks(const std::vector<float>& ecg_signal, float sample_rate) ;
+    std::vector<float> detect_pulse_peaks(const std::vector<float>& ppg_signal, float sample_rate);
+};   
+#endif

include/signal_processor.h

@@ -64,6 +64,11 @@ public:
     // 预处理信号
     SensorData preprocess_signals(const SensorData& raw_data);  // 使用DataType枚举
     SensorData preprocess_generic(const SensorData& data);
+    
+    // 新增：通道级滤波处理
+    std::vector<SensorData> process_channel_based_filtering(
+        const std::vector<SensorData>& data_packets);
+    
     // 特征提取
     FeatureSet extract_signal_features(const SensorData& processed_data, 
                                       const std::vector<std::string>& features = {});
@@ -150,6 +155,23 @@ public:
     float calculate_PPG_sqi(const std::vector<float>& red_channel, 
                                         const std::vector<float>& ir_channel);
     float calculate_ecg_sqi(const std::vector<float>& signal, double sample_rate);
+    
+    // 新增：通道级滤波辅助方法
+private:
+    std::vector<std::vector<float>> apply_channel_filters(
+        const std::vector<std::vector<float>>& channels, DataType data_type);
+    std::vector<std::vector<float>> apply_eeg_filters(
+        const std::vector<std::vector<float>>& channels);
+    std::vector<std::vector<float>> apply_ecg_filters(
+        const std::vector<std::vector<float>>& channels);
+    std::vector<std::vector<float>> apply_ppg_filters(
+        const std::vector<std::vector<float>>& channels);
+    std::vector<std::vector<float>> apply_respiration_filters(
+        const std::vector<std::vector<float>>& channels);
+    std::vector<std::vector<float>> apply_snore_filters(
+        const std::vector<std::vector<float>>& channels);
+    std::vector<std::vector<float>> apply_stethoscope_filters(
+        const std::vector<std::vector<float>>& channels);
 };
 
 

include/types.h

@@ -12,7 +12,8 @@ enum class DataType {
     PPG,            // 血氧
     RESPIRATION,    // 呼吸/姿态
     SNORE,          // 鼾声
-    STETHOSCOPE     // 数字听诊
+    STETHOSCOPE,     // 数字听诊
+    MIT_BIH         // 添加MIT-BIH心律失常数据集类型
 };
 
 // 导联脱落状态

main.cpp

@@ -1,4 +1,5 @@
 #include "headfile.h"
+std::vector<float> heart_rate;
 // 辅助函数：打印多通道数据
 void print_multi_channel(const std::vector<std::vector<float>>& channels) {
     for (size_t ch = 0; ch < channels.size(); ++ch) {
@@ -19,32 +20,51 @@ void print_channel_data(const std::variant<std::vector<float>, std::vector<std::
         print_multi_channel(multi_channel);
     }
 }
+
+void test_mit_bih() {
+    try {
+        // 读取MIT-BIH数据文件
+        std::vector<uint8_t> file_content = FileManager::readBinaryFile("C:/Users/29096/Desktop/work/mit-bih-arrhythmia-database-1.0.0/222.dat");
+        
+        // 解析数据
+        std::vector<SensorData> all_data = parse_device_data(file_content);
+
+        // 保存结果
+        save_to_csv(all_data, "mit_bih_output.csv");
+        
+        std::cout << "MIT-BIH数据处理完成" << std::endl;
+        std::cout << "Press Enter to exit..." << std::endl;
+        std::cin.get();
+    } catch (const std::exception& e) {
+        std::cerr << "处理错误: " << e.what() << std::endl;
+    }
+}
+
 void test_try() {
     try {
         // 1. 读取原始二进制文件    
         std::vector<uint8_t> file_content = FileManager::readBinaryFile("C:/Users/29096/Documents/WeChat Files/wxid_sh93l5lycr8b22/FileStorage/File/2025-07/ecg_data_raw.dat");
         Mapper mapper;
+        SignalProcessor processor;
+        MetricsCalculator calculator;
         // 2. 解析设备数据包 - 需要实现此函数
         std::vector<SensorData> all_data = parse_device_data(file_content);
-        save_to_csv(all_data, "channel_data_transered_.csv");
-        // 3. 创建信号处理器实例
-       /* SignalProcessor processor;
-        // 4. 遍历所有数据包
-        for (auto& data : all_data) {
-            // 打印原始数据信息
-           // print_parsed_result(data); // 需要实现此函数
-          std::cout << "Before mapping (ECG-12):" << std::endl;
-        std::cout << "Packet SN: " << data.packet_sn << ", Data Type: " << static_cast<int>(data.data_type) << std::endl;*/
-       // print_channel_data(data.channel_data);
-       /* SensorData mapped_ecg_12lead = mapper.DataMapper(data);
-        SensorData processed_ecg_12_lead = processor.preprocess_signals(mapped_ecg_12lead);
-        std::cout << "After mapping (ECG-12):" << std::endl;
-        std::cout << "Packet SN: " << mapped_ecg_12lead.packet_sn << ", Data Type: " << static_cast<int>(mapped_ecg_12lead.data_type) << std::endl;
-            print_channel_data(mapped_ecg_12lead.channel_data);
-        std::cout << "After processed (ECG-12):" << std::endl;
-        std::cout << "Packet SN: " << mapped_ecg_12lead.packet_sn << ", Data Type: " << static_cast<int>(mapped_ecg_12lead.data_type) << std::endl;
-            print_channel_data(processed_ecg_12_lead.channel_data);
-    }*/
+        std::cout<<"1"<<std::endl;
+        for(auto& mapped_data:all_data)
+        {
+            mapped_data = mapper.DataMapper(mapped_data);//通道映射
+        }
+        save_to_csv(all_data, "channel_data_mapped_.csv");
+        for(auto& processed_data:all_data)
+        {
+            processed_data = processor.preprocess_signals(processed_data);  //十二导联心电
+        }
+        for(auto& calculated_data:all_data)
+        {
+            heart_rate.push_back(calculator.calculate_heart_rate_ecg(calculated_data,250));  //十二导联心电
+        }
+        for(uint16_t i;i<heart_rate.size();i++) std::cout<<heart_rate[i]<<std::endl;
+        save_to_csv(all_data, "channel_data_processed_.csv");
         
     }catch (const std::exception& e) {
         std::cerr << "解析错误: " << e.what() << std::endl;
@@ -57,7 +77,6 @@ void test_try() {
 }
 int main() {
     SetConsoleOutputCP(CP_UTF8);
-    test_try();
+    test_try(); // 测试MIT-BIH数据处理
     return 0;
 }
-        

src/Tool/File_manage.cpp

@@ -84,3 +84,11 @@ void save_to_csv(const std::vector<SensorData>& all_data, const std::string& fil
     outfile.close();
     std::cout << "数据已保存至: " << filename << std::endl;
 }
+// 检测是否为MIT-BIH文件
+bool FileManager::is_mit_bih_file(const std::vector<uint8_t>& data) {
+    // 简单启发式检测：检查文件大小是否匹配典型MIT-BIH格式
+    // 实际应用中应该解析头文件(.hea)获取确切信息
+    const size_t size = data.size();
+    std::cout<<size<<std::endl;
+    return (size > 1024 && size % (2 * sizeof(int16_t)) == 0);
+}

src/data_output/indicator_cal.cpp

@@ -0,0 +1,313 @@
+// metrics_calculator.cpp
+#include "indicator_cal.h"
+#include <cmath>
+#include <algorithm>
+#include <numeric>
+// 辅助函数：从SensorData获取单通道数据
+static const std::vector<float>& get_single_channel(const SensorData& data) {
+    if (std::holds_alternative<std::vector<float>>(data.channel_data)) {
+        return std::get<std::vector<float>>(data.channel_data);
+    } else {
+        const auto& channels = std::get<std::vector<std::vector<float>>>(data.channel_data);
+        if (!channels.empty()) {
+            return channels[0]; // 返回第一通道
+        }
+        static const std::vector<float> empty;
+        return empty;
+    }
+}
+
+// ECG心率计算
+float MetricsCalculator::calculate_heart_rate_ecg(const SensorData& ecg_signal, float sample_rate) {
+    const auto& signal = get_single_channel(ecg_signal);
+    if (signal.size() < 3 || sample_rate <= 0) return 0.0f;
+    
+    // 检测R峰
+    auto r_peaks = detect_r_peaks(signal, sample_rate);
+    if (r_peaks.size() < 2) return 0.0f; // 至少需要2个R峰计算心率
+    
+    // 计算平均RR间期
+    float total_rr = 0.0f;
+    for (size_t i = 1; i < r_peaks.size(); i++) {
+        total_rr += r_peaks[i] - r_peaks[i-1];
+    }
+    float avg_rr = total_rr / (r_peaks.size() - 1);
+    
+    // 转换为心率 (次/分钟)
+    return 60.0f / (avg_rr / sample_rate);
+}
+
+
+// T波振幅计算
+float MetricsCalculator::calculate_t_wave_amplitude(const std::vector<float>& ecg_signal) {
+    if (ecg_signal.empty()) return 0.0f;
+    
+    // 计算QRS波群后的平均振幅（代表T波）
+    const size_t window_start = static_cast<size_t>(ecg_signal.size() * 0.3); // QRS后约30%位置
+    const size_t window_end = static_cast<size_t>(ecg_signal.size() * 0.5);   // 50%位置
+    
+    float max_amplitude = 0.0f;
+    for (size_t i = window_start; i < window_end && i < ecg_signal.size(); i++) {
+        if (std::abs(ecg_signal[i]) > max_amplitude) {
+            max_amplitude = std::abs(ecg_signal[i]);
+        }
+    }
+    return max_amplitude;
+}
+
+// PPG心率计算
+float MetricsCalculator::calculate_heart_rate_ppg(const std::vector<float>& ppg_signal, float sample_rate) {
+    if (ppg_signal.size() < 3 || sample_rate <= 0) return 0.0f;
+    
+    // 检测脉搏波峰
+    auto pulse_peaks = detect_pulse_peaks(ppg_signal, sample_rate);
+    if (pulse_peaks.size() < 2) return 0.0f;
+    
+    // 计算平均脉搏间期
+    float total_intervals = 0.0f;
+    for (size_t i = 1; i < pulse_peaks.size(); i++) {
+        total_intervals += pulse_peaks[i] - pulse_peaks[i-1];
+    }
+    float avg_interval = total_intervals / (pulse_peaks.size() - 1);
+    
+    // 转换为心率 (次/分钟)
+    return 60.0f / (avg_interval / sample_rate);
+}
+
+// 血氧饱和度计算
+float MetricsCalculator::calculate_spo2(const SensorData& ppg_data) {
+    if (!std::holds_alternative<std::vector<std::vector<float>>>(ppg_data.channel_data) || 
+        std::get<std::vector<std::vector<float>>>(ppg_data.channel_data).size() < 2) {
+        return 0.0f;
+    }
+    
+    const auto& channels = std::get<std::vector<std::vector<float>>>(ppg_data.channel_data);
+    const auto& red_channel = channels[0];
+    const auto& ir_channel = channels[1];
+    
+    // 计算红光和红外光的AC/DC分量
+    auto calc_ac_dc = [](const std::vector<float>& signal) -> std::pair<float, float> {
+        float max_val = *std::max_element(signal.begin(), signal.end());
+        float min_val = *std::min_element(signal.begin(), signal.end());
+        float dc = (max_val + min_val) / 2.0f;
+        float ac = (max_val - min_val) / 2.0f;
+        return {ac, dc};
+    };
+    
+    auto [red_ac, red_dc] = calc_ac_dc(red_channel);
+    auto [ir_ac, ir_dc] = calc_ac_dc(ir_channel);
+    
+    // 计算R值 (红光AC/DC与红外光AC/DC的比值)
+    float r_value = (red_ac / red_dc) / (ir_ac / ir_dc);
+    
+    // 经验公式计算SpO2 (需要根据设备校准)
+    return 110.0f - 25.0f * r_value;
+}
+
+// 脉搏波宽度计算
+float MetricsCalculator::calculate_pulse_width(const std::vector<float>& ppg_signal) {
+    if (ppg_signal.empty()) return 0.0f;
+    
+    // 找到最大峰值位置
+    auto max_it = std::max_element(ppg_signal.begin(), ppg_signal.end());
+    size_t peak_idx = std::distance(ppg_signal.begin(), max_it);
+    
+    // 找到上升沿起点 (信号值小于峰值的10%)
+    size_t start_idx = peak_idx;
+    while (start_idx > 0 && ppg_signal[start_idx] > 0.1f * *max_it) {
+        start_idx--;
+    }
+    
+    // 找到下降沿终点 (信号值小于峰值的10%)
+    size_t end_idx = peak_idx;
+    while (end_idx < ppg_signal.size() - 1 && ppg_signal[end_idx] > 0.1f * *max_it) {
+        end_idx++;
+    }
+    
+    return static_cast<float>(end_idx - start_idx);
+}
+
+// 红光/红外光振幅比
+float MetricsCalculator::calculate_amplitude_ratio(const SensorData& ppg_data) {
+    if (!std::holds_alternative<std::vector<std::vector<float>>>(ppg_data.channel_data) || 
+        std::get<std::vector<std::vector<float>>>(ppg_data.channel_data).size() < 2) {
+        return 0.0f;
+    }
+    
+    const auto& channels = std::get<std::vector<std::vector<float>>>(ppg_data.channel_data);
+    const auto& red_channel = channels[0];
+    const auto& ir_channel = channels[1]; 
+    
+    float red_amp = *std::max_element(red_channel.begin(), red_channel.end()) - 
+                   *std::min_element(red_channel.begin(), red_channel.end());
+    
+    float ir_amp = *std::max_element(ir_channel.begin(), ir_channel.end()) - 
+                  *std::min_element(ir_channel.begin(), ir_channel.end());
+    
+    return (ir_amp > 0.0001f) ? red_amp / ir_amp : 0.0f;
+}
+
+// HRV指标 - SDNN (RR间期的标准差)
+float MetricsCalculator::calculate_sdnn(const std::vector<float>& rr_intervals) {
+    if (rr_intervals.size() < 2) return 0.0f;
+    
+    float mean = std::accumulate(rr_intervals.begin(), rr_intervals.end(), 0.0f) / rr_intervals.size();
+    
+    float variance = 0.0f;
+    for (float rr : rr_intervals) {
+        variance += (rr - mean) * (rr - mean);
+    }
+    variance /= rr_intervals.size();
+    
+    return std::sqrt(variance);
+}
+
+// HRV指标 - RMSSD (相邻RR间期差值的均方根)
+float MetricsCalculator::calculate_rmssd(const std::vector<float>& rr_intervals) {
+    if (rr_intervals.size() < 2) return 0.0f;
+    
+    float sum_sq_diff = 0.0f;
+    for (size_t i = 1; i < rr_intervals.size(); i++) {
+        float diff = rr_intervals[i] - rr_intervals[i-1];
+        sum_sq_diff += diff * diff;
+    }
+    
+    return std::sqrt(sum_sq_diff / (rr_intervals.size() - 1));
+}
+
+// 改进的R峰检测算法 (基于Pan-Tompkins算法)
+std::vector<float> MetricsCalculator::detect_r_peaks(const std::vector<float>& ecg_signal, float sample_rate) {
+    std::vector<float> r_peaks;
+    if (ecg_signal.size() < 5 || sample_rate <= 0) return r_peaks;
+    
+    const size_t n = ecg_signal.size();
+    
+    // 1. 计算积分窗口大小 (150ms窗口)
+    const int win_integ = static_cast<int>(0.15f * sample_rate + 0.5f); // 四舍五入
+    if (win_integ < 1) return r_peaks;
+    
+    // 有效数据起始位置 (滤波+积分导致的延迟)
+    const size_t start_index = 5 + win_integ - 1;
+
+    // 2. 带通滤波 (通过移动平均近似)
+    std::vector<float> filtered(n, 0.0f);
+    // 低通滤波 (5点移动平均)
+    for (size_t i = 4; i < n; i++) {
+        filtered[i] = (ecg_signal[i-4] + ecg_signal[i-3] + 
+                       ecg_signal[i-2] + ecg_signal[i-1] + 
+                       ecg_signal[i]) / 5.0f;
+    }
+    
+    // 3. 微分 (增强QRS斜率)
+    std::vector<float> diff(n, 0.0f);
+    for (size_t i = 5; i < n; i++) {
+        diff[i] = filtered[i] - filtered[i-1];
+    }
+    
+    // 4. 平方 (放大高频分量)
+    std::vector<float> sqrd(n, 0.0f);
+    for (size_t i = 0; i < n; i++) {
+        sqrd[i] = diff[i] * diff[i];
+    }
+    
+    // 5. 移动平均积分 (突出QRS复合波)
+    std::vector<float> integrated(n, 0.0f);
+    for (size_t i = start_index; i < n; i++) {
+        float sum = 0.0f;
+        for (int j = 0; j < win_integ; j++) {
+            sum += sqrd[i - j];
+        }
+        integrated[i] = sum / win_integ;
+    }
+    
+    // 6. 自适应阈值检测
+    float threshold = 0.0f;
+    float peak_value = 0.0f;
+    const size_t min_interval = static_cast<size_t>(0.2f * sample_rate); // 200ms最小间隔
+    
+    // 初始阈值设置 (使用前2秒数据)
+    const size_t init_win = std::min(static_cast<size_t>(2 * sample_rate), n - start_index);
+    if (init_win > 10) {
+        auto max_it = std::max_element(integrated.begin() + start_index, 
+                                      integrated.begin() + start_index + init_win);
+        threshold = 0.5f * (*max_it);
+    } else {
+        threshold = 0.5f * (*std::max_element(integrated.begin(), integrated.end()));
+    }
+    
+    // 噪声和信号峰值跟踪
+    float noise_peak = 0.25f * threshold;
+    float signal_peak = threshold;
+    const float decay_rate = 0.125f; // 阈值衰减率
+    
+    size_t last_peak = 0;
+    bool found_peak = false;
+    
+    for (size_t i = start_index + 1; i < n - 1; i++) {
+        // 检测峰值 (大于前后两点)
+        if (integrated[i] > integrated[i-1] && 
+            integrated[i] > integrated[i+1] && 
+            integrated[i] > threshold) {
+            
+            // 检查最小间隔
+            if (last_peak == 0 || i - last_peak > min_interval) {
+                r_peaks.push_back(static_cast<float>(i));
+                last_peak = i;
+                found_peak = true;
+                peak_value = integrated[i];
+            }
+        }
+        
+        // 自适应阈值更新
+        if (found_peak) {
+            // 检测到峰值后更新信号阈值
+            signal_peak = decay_rate * peak_value + (1 - decay_rate) * signal_peak;
+            found_peak = false;
+        } else {
+            // 未检测到峰值时更新噪声阈值
+            if (integrated[i] > noise_peak) {
+                noise_peak = decay_rate * integrated[i] + (1 - decay_rate) * noise_peak;
+            }
+        }
+        
+        // 组合阈值 (信号和噪声阈值的加权平均)
+        threshold = 0.25f * signal_peak + 0.75f * noise_peak;
+    }
+    
+    return r_peaks;
+}
+
+// 脉搏波峰检测
+std::vector<float> MetricsCalculator::detect_pulse_peaks(const std::vector<float>& ppg_signal, float sample_rate) {
+    std::vector<float> pulse_peaks;
+    if (ppg_signal.empty()) return pulse_peaks;
+    
+    // 自适应阈值
+    float threshold = 0.6f * (*std::max_element(ppg_signal.begin(), ppg_signal.end()));
+    
+    // 最小脉搏间隔 (300ms ≈ 200bpm)
+    const size_t min_interval = static_cast<size_t>(0.3f * sample_rate);
+    
+    bool in_peak = false;
+    size_t last_peak = 0;
+    
+    for (size_t i = 1; i < ppg_signal.size() - 1; i++) {
+        // 检测上升沿
+        if (!in_peak && ppg_signal[i] > threshold && 
+            ppg_signal[i] > ppg_signal[i-1] && ppg_signal[i] > ppg_signal[i+1]) {
+            
+            if (i - last_peak > min_interval || last_peak == 0) {
+                pulse_peaks.push_back(static_cast<float>(i));
+                last_peak = i;
+                in_peak = true;
+            }
+        }
+        
+        // 检测下降沿
+        if (in_peak && ppg_signal[i] < 0.7f * threshold) {
+            in_peak = false;
+        }
+    }   
+    
+    return pulse_peaks;
+}

src/data_receiver/data_mapper.cpp

@@ -104,7 +104,7 @@ SensorData Mapper::ECG_12LEAD_Data_Mapper(SensorData& data)
     // 处理前8个通道
     for (int ch = 0; ch < 8; ++ch) {
         for (int sample = 0; sample < 14; ++sample) {
-            channels[ch][sample] = ecg_data[ch * 14 + sample] * 0.318f;
+            channels[ch][sample] = ecg_data[ch * 14 + sample]*0.318;
         }
     }
 

src/data_receiver/protocol_adapter/device_praser.cpp

@@ -220,6 +220,52 @@ SensorData parse_12lead_ecg(const uint8_t* data) {
     return result;
 }
 
+// MIT-BIH 212格式解析器
+SensorData parse_mit_bih_212(const uint8_t* data, size_t size) {
+    SensorData result;
+    result.data_type = DataType::MIT_BIH;
+    result.packet_sn = 0; // 单文件数据，SN设为0
+    result.lead_status = {{0}}; // 无导联状态
+    result.timestamp = 0; // 无时间戳
+    
+    // 检查文件大小是否合理
+    if (size < 1024 || size % 2 != 0) {
+        throw std::runtime_error("无效的MIT-BIH文件大小");
+    }
+    
+    // 解析212格式数据
+    auto& channels = result.channel_data.emplace<std::vector<std::vector<float>>>();
+    channels.resize(2); // 两个导联
+    
+    // 212格式: 每2字节包含两个12位样本
+    for (size_t i = 0; i < size; i += 2) {
+        uint8_t byte1 = data[i];
+        uint8_t byte2 = data[i+1];
+        
+        // 解析第一个通道 (低12位)
+        int16_t sample1 = ((byte1 & 0x0F) << 8) | byte2;
+        // 符号扩展 (12位有符号 -> 16位有符号)
+        if (sample1 & 0x0800) sample1 |= 0xF000;
+        
+        // 解析第二个通道 (高12位)
+        int16_t sample2 = ((byte1 & 0xF0) << 4) | (byte2 >> 4);
+        // 符号扩展 (12位有符号 -> 16位有符号)
+        if (sample2 & 0x0800) sample2 |= 0xF000;
+        
+        // 转换为毫伏 (MIT-BIH ADC范围: ±5mV = ±2048)
+        float mv1 = sample1 * (5.0f / 2048.0f);
+        float mv2 = sample2 * (5.0f / 2048.0f);
+        
+        channels[0].push_back(mv1);
+        channels[1].push_back(mv2);
+    }
+    
+    // 存储原始数据
+    result.raw_data.assign(data, data + size);
+    
+    return result;
+}
+
 // 数字听诊解析 (0x1102)
 SensorData parse_stethoscope(const uint8_t* data) {
     SensorData result;
@@ -309,6 +355,7 @@ SensorData parse_respiration(const uint8_t* data) {
 
 // 统一解析入口函数 - 支持多个帧头和数据包组
 std::vector<SensorData> parse_device_data(const std::vector<uint8_t>& file_data) {
+    FileManager manager_;
     const size_t PACKET_SIZE = 238;  // 每个数据包固定大小
     const size_t RESPONSE_HEADER_SIZE = 10; // 响应帧头大小 (2功能码 + 2数据长度 + 4实际采集点数+crc校验)
     const uint16_t FUNCTION_CODE = 0x0010; // 获取数据功能码
@@ -316,9 +363,8 @@ std::vector<SensorData> parse_device_data(const std::vector<uint8_t>& file_data)
     const size_t file_size = file_data.size();
     const uint8_t* ptr = file_data.data();
     const uint8_t* end_ptr = ptr + file_size;
-    
+    std::map<DataType, std::vector<SensorData>> grouped_data;
     std::vector<SensorData> results;
-    
     while (ptr < end_ptr) {
         // 检查是否有响应帧头
         bool has_response_header = false;
@@ -440,6 +486,39 @@ std::vector<SensorData> parse_device_data(const std::vector<uint8_t>& file_data)
             }
         }
     }
+    // 将解析的结果按数据类型分组
+    for (const auto& result : results) {
+        grouped_data[result.data_type].push_back(result);
+    }
+    for (auto& [data_type, packets] : grouped_data) {
+        if (packets.empty()) continue;
         
+        // 创建新的SensorData对象，包含完整通道数据
+        SensorData full_data = packets[0];
+        full_data.channel_data = std::vector<std::vector<float>>();
+        
+        // 获取通道数量
+        size_t num_channels = 0;
+        if (auto* channels = std::get_if<std::vector<std::vector<float>>>(&packets[0].channel_data)) {
+            num_channels = channels->size();
+            full_data.channel_data.emplace<std::vector<std::vector<float>>>(num_channels);
+        }
+        
+        // 合并所有数据包中的通道数据
+        auto& full_channels = std::get<std::vector<std::vector<float>>>(full_data.channel_data);
+        for (size_t ch = 0; ch < num_channels; ch++) {
+            for (auto& packet : packets) {
+                if (auto* channels = std::get_if<std::vector<std::vector<float>>>(&packet.channel_data)) {
+                    if (ch < channels->size()) {
+                        full_channels[ch].insert(full_channels[ch].end(), 
+                                                (*channels)[ch].begin(), 
+                                                (*channels)[ch].end());
+                    }
+                }
+            }
+        }
+        
+        results.push_back(full_data);
+    }
     return results;
 }

src/signal_processor/signal_processor.cpp

@@ -1,4 +1,264 @@
 #include "signal_processor.h"
+
+// 新增：处理多个数据包的通道级滤波
+std::vector<SensorData> SignalProcessor::process_channel_based_filtering(
+    const std::vector<SensorData>& data_packets) {
+    
+    if (data_packets.empty()) return {};
+    
+    // 按数据类型分组
+    std::map<DataType, std::vector<SensorData>> grouped_data;
+    for (const auto& packet : data_packets) {
+        grouped_data[packet.data_type].push_back(packet);
+    }
+    
+    std::vector<SensorData> processed_packets;
+    
+    // 对每种数据类型分别处理
+    for (auto& [data_type, packets] : grouped_data) {
+        if (packets.empty()) continue;
+        
+        // 获取第一个数据包作为模板
+        SensorData template_packet = packets[0];
+        
+        // 收集所有通道的完整数据
+        std::vector<std::vector<float>> all_channels;
+        size_t num_channels = 0;
+        
+        // 确定通道数量
+        if (auto* channels = std::get_if<std::vector<std::vector<float>>>(&packets[0].channel_data)) {
+            num_channels = channels->size();
+            all_channels.resize(num_channels);
+        }
+        
+        // 收集所有数据包中相同通道的数据
+        for (size_t ch = 0; ch < num_channels; ch++) {
+            for (const auto& packet : packets) {
+                if (auto* channels = std::get_if<std::vector<std::vector<float>>>(&packet.channel_data)) {
+                    if (ch < channels->size()) {
+                        all_channels[ch].insert(all_channels[ch].end(), 
+                                              (*channels)[ch].begin(), 
+                                              (*channels)[ch].end());
+                    }
+                }
+            }
+        }
+        
+        // 对完整通道数据进行滤波处理
+        std::vector<std::vector<float>> filtered_channels = 
+            apply_channel_filters(all_channels, data_type);
+        
+        // 将滤波后的数据重新分配回数据包结构
+        size_t samples_per_packet = 0;
+        if (auto* channels = std::get_if<std::vector<std::vector<float>>>(&packets[0].channel_data)) {
+            if (!channels->empty()) {
+                samples_per_packet = (*channels)[0].size();
+            }
+        }
+        
+        // 重新构建数据包
+        size_t total_samples = filtered_channels[0].size();
+        size_t num_packets = (total_samples + samples_per_packet - 1) / samples_per_packet;
+        
+        for (size_t p = 0; p < num_packets; p++) {
+            SensorData new_packet = template_packet;
+            new_packet.packet_sn = p; // 重新分配包序号
+            
+            // 创建新的通道数据结构
+            auto& new_channels = new_packet.channel_data.emplace<std::vector<std::vector<float>>>();
+            new_channels.resize(num_channels);
+            
+            // 分配数据到各个通道
+            for (size_t ch = 0; ch < num_channels; ch++) {
+                size_t start_idx = p * samples_per_packet;
+                size_t end_idx = std::min(start_idx + samples_per_packet, filtered_channels[ch].size());
+                
+                if (start_idx < filtered_channels[ch].size()) {
+                    new_channels[ch].assign(filtered_channels[ch].begin() + start_idx,
+                                           filtered_channels[ch].begin() + end_idx);
+                }
+            }
+            
+            processed_packets.push_back(new_packet);
+        }
+    }
+    
+    return processed_packets;
+}
+
+// 新增：对完整通道数据应用滤波器
+std::vector<std::vector<float>> SignalProcessor::apply_channel_filters(
+    const std::vector<std::vector<float>>& channels, DataType data_type) {
+    
+    std::vector<std::vector<float>> filtered_channels = channels;
+    
+    switch (data_type) {
+        case DataType::EEG:
+            filtered_channels = apply_eeg_filters(channels);
+            break;
+        case DataType::ECG_2LEAD:
+        case DataType::ECG_12LEAD:
+            filtered_channels = apply_ecg_filters(channels);
+            break;
+        case DataType::PPG:
+            filtered_channels = apply_ppg_filters(channels);
+            break;
+        case DataType::RESPIRATION:
+            filtered_channels = apply_respiration_filters(channels);
+            break;
+        case DataType::SNORE:
+            filtered_channels = apply_snore_filters(channels);
+            break;
+        case DataType::STETHOSCOPE:
+            filtered_channels = apply_stethoscope_filters(channels);
+            break;
+        default:
+            // 通用滤波
+            for (auto& channel : filtered_channels) {
+                channel = bandpass_filter(channel, 250.0, 0.5, 45.0);
+            }
+            break;
+    }
+    
+    return filtered_channels;
+}
+
+// 新增：EEG通道滤波
+std::vector<std::vector<float>> SignalProcessor::apply_eeg_filters(
+    const std::vector<std::vector<float>>& channels) {
+    
+    const double SAMPLE_RATE = 250.0;
+    std::vector<std::vector<float>> filtered_channels = channels;
+    
+    if (channels.size() < 8) return filtered_channels;
+    
+    // 分离EEG和EOG通道
+    std::vector<std::vector<float>> eeg_channels(channels.begin(), channels.begin() + 6);
+    std::vector<std::vector<float>> eog_channels(channels.begin() + 6, channels.end());
+    
+    // 处理EEG通道
+    for (auto& channel : eeg_channels) {
+        // 眼电伪迹补偿
+        if (eog_channels.size() >= 2) {
+            channel = compensate_eog_artifact(channel, eog_channels[0], eog_channels[1]);
+        }
+        
+        // 50Hz自适应陷波滤波
+        channel = adaptive_notch_filter(channel, SAMPLE_RATE, 50.0, 5.0);
+        
+        // 0.5-45Hz带通滤波
+        channel = bandpass_filter(channel, SAMPLE_RATE, 0.5, 45.0);
+    }
+    
+    // 处理EOG通道
+    for (auto& channel : eog_channels) {
+        channel = bandpass_filter(channel, SAMPLE_RATE, 0.5, 30.0);
+    }
+    
+    // 合并处理后的通道
+    filtered_channels.clear();
+    filtered_channels.insert(filtered_channels.end(), eeg_channels.begin(), eeg_channels.end());
+    filtered_channels.insert(filtered_channels.end(), eog_channels.begin(), eog_channels.end());
+    
+    return filtered_channels;
+}
+
+// 新增：ECG通道滤波
+std::vector<std::vector<float>> SignalProcessor::apply_ecg_filters(
+    const std::vector<std::vector<float>>& channels) {
+    
+    const double SAMPLE_RATE = 250.0;
+    std::vector<std::vector<float>> filtered_channels = channels;
+    
+    for (auto& channel : filtered_channels) {
+        // 0.5Hz高通滤波
+        channel = Highpass_filter(channel, SAMPLE_RATE, 0.5);
+        
+        // 50Hz自适应陷波滤波
+        channel = adaptive_notch_filter(channel, SAMPLE_RATE, 50.0, 5.0);
+        
+        // 25-40Hz带阻滤波
+        channel = bandstop_filter(channel, SAMPLE_RATE, 25.0, 40.0);
+    }
+    
+    return filtered_channels;
+}
+
+// 新增：PPG通道滤波
+std::vector<std::vector<float>> SignalProcessor::apply_ppg_filters(
+    const std::vector<std::vector<float>>& channels) {
+    
+    const double SAMPLE_RATE = 50.0;
+    std::vector<std::vector<float>> filtered_channels = channels;
+    
+    for (auto& channel : filtered_channels) {
+        // 移除直流分量
+        channel = remove_dc_offset(channel);
+        
+        // 0.5-10Hz带通滤波
+        channel = bandpass_filter(channel, SAMPLE_RATE, 0.5, 10.0);
+    }
+    
+    return filtered_channels;
+}
+
+// 新增：呼吸通道滤波
+std::vector<std::vector<float>> SignalProcessor::apply_respiration_filters(
+    const std::vector<std::vector<float>>& channels) {
+    
+    const double SAMPLE_RATE = 100.0;
+    std::vector<std::vector<float>> filtered_channels = channels;
+    
+    for (auto& channel : filtered_channels) {
+        // 0.1Hz高通滤波
+        channel = filter(channel, SAMPLE_RATE, 0, 0.1, filtertype::highpass);
+        
+        // 50Hz陷波滤波
+        channel = adaptive_notch_filter(channel, SAMPLE_RATE, 50.0, 5.0);
+        
+        // 振幅归一化
+        normalize_amplitude(channel);
+    }
+    
+    return filtered_channels;
+}
+
+// 新增：打鼾通道滤波
+std::vector<std::vector<float>> SignalProcessor::apply_snore_filters(
+    const std::vector<std::vector<float>>& channels) {
+    
+    const double SAMPLE_RATE = 2000.0;
+    std::vector<std::vector<float>> filtered_channels = channels;
+    
+    for (auto& channel : filtered_channels) {
+        // 50-2000Hz带通滤波
+        channel = bandpass_filter(channel, SAMPLE_RATE, 50.0, 2000.0);
+        
+        // 振幅归一化
+        normalize_amplitude(channel);
+    }
+    
+    return filtered_channels;
+}
+
+// 新增：听诊器通道滤波
+std::vector<std::vector<float>> SignalProcessor::apply_stethoscope_filters(
+    const std::vector<std::vector<float>>& channels) {
+    
+    const double SAMPLE_RATE = 4000.0;
+    std::vector<std::vector<float>> filtered_channels = channels;
+    
+    for (auto& channel : filtered_channels) {
+        // 20-2000Hz带通滤波
+        channel = bandpass_filter(channel, SAMPLE_RATE, 20.0, 2000.0);
+        
+        // 振幅归一化
+        normalize_amplitude(channel);
+    }
+    
+    return filtered_channels;
+}
+
 SensorData SignalProcessor::preprocess_generic(const SensorData& data) {
     SensorData processed = data;
     
@@ -142,16 +402,18 @@ SensorData SignalProcessor::preprocess_ecg_12lead(const SensorData& data) {
         throw std::runtime_error("Invalid channel count for 12-lead ECG");
     }
 
+    
     // 对每个导联独立进行信号处理
     for (auto& channel : channels) {
         // 1. 0.5Hz高通滤波 (去除基线漂移)
-        channel = filter(channel, SAMPLE_RATE, 0.5, 0.0, filtertype::highpass);
+        //channel = remove_dc_offset(channel);
+        channel = filter(channel, SAMPLE_RATE,0,0.5, filtertype::highpass);
         
         // 2. 50Hz自适应陷波滤波 (去除工频干扰)
-        channel = filter(channel, SAMPLE_RATE, 50.0, 60,filtertype::notchpass);
+        channel = filter(channel, SAMPLE_RATE, 49.5, 51.5, filtertype::notchpass);
         
         // 3. 25-40Hz带阻滤波 (去除肌电干扰)
-        channel = filter(channel, SAMPLE_RATE, 25.0, 40.0, filtertype::bandstop);
+        channel = filter(channel, SAMPLE_RATE, 0.5, 0.6, filtertype::bandstop);
     }
     
     // 计算并存储信号质量指数
@@ -291,8 +553,7 @@ std::vector<float> SignalProcessor::remove_dc_offset(const std::vector<float>& s
     float dc_remove = 0;
     for(auto& val:signal) dc_remove += val;
     dc_remove /= result.size();
-    for(auto& value:result) value -= dc_remove;  
-    return result;
+    for(auto& value:result) value -= dc_remove;      return result;
 }
 std::vector<float> SignalProcessor::apply_gain(const std::vector<float>& signal, float gain) {
     std::vector<float> result = signal;
@@ -310,45 +571,53 @@ std::vector<float> SignalProcessor::adaptive_notch_filter(const std::vector<floa
                                     double sample_rate,
                                     double target_freq,
                                     double bandwidth) {
-    std::vector<float> output(input.size(), 0.0f);
+    if (input.empty()) return {};
 
-    // 计算滤波器系数
-    double omega0 = 2 * M_PI * target_freq / sample_rate;
-    double alpha = sin(omega0) * sinh(log(2) / 2 * bandwidth * omega0 / sin(omega0));
+    // 使用更稳定的实现
+    const double omega0 = 2 * M_PI * target_freq / sample_rate;
+    const double alpha = sin(omega0) * sinh(log(2) / 2 * bandwidth * omega0 / sin(omega0));
 
-    double b0 = 1;
-    double b1 = -2 * cos(omega0);
-    double b2 = 1;
-    double a0 = 1 + alpha;
-    double a1 = -2 * cos(omega0);
-    double a2 = 1 - alpha;
+    // 系数计算
+    const double b0 = 1.0;
+    const double b1 = -2 * cos(omega0);
+    const double b2 = 1.0;
+    const double a0 = 1 + alpha;
+    const double a1 = -2 * cos(omega0);
+    const double a2 = 1 - alpha;
 
     // 归一化系数
-    b0 /= a0;
-    b1 /= a0;
-    b2 /= a0;
-    a1 /= a0;
-    a2 /= a0;
-
-    // 初始化滤波器状态
-    double x1 = 0, x2 = 0;
-    double y1 = 0, y2 = 0;
+    const double inv_a0 = 1.0 / a0;
+    const double nb0 = b0 * inv_a0;
+    const double nb1 = b1 * inv_a0;
+    const double nb2 = b2 * inv_a0;
+    const double na1 = a1 * inv_a0;
+    const double na2 = a2 * inv_a0;
 
     // 应用滤波器
+    std::vector<float> output(input.size());
+    double x1 = 0.0, x2 = 0.0;
+    double y1 = 0.0, y2 = 0.0;
+
     for (size_t n = 0; n < input.size(); ++n) {
         double x0 = input[n];
-        output[n] = b0 * x0 + b1 * x1 + b2 * x2 - a1 * y1 - a2 * y2;
+        double y = nb0 * x0 + nb1 * x1 + nb2 * x2 - na1 * y1 - na2 * y2;
+        
+        // 防止不稳定
+        if (!std::isfinite(y)) y = 0.0;
+        
+        output[n] = static_cast<float>(y);
         
         // 更新状态
         x2 = x1;
         x1 = x0;
         y2 = y1;
-        y1 = output[n];
+        y1 = y;
     }
 
     return output;
 }
 
+
 std::vector<float> SignalProcessor::filter(const std::vector<float>& input,
                                             double sample_rate,    
                                             double low_cutoff,  
@@ -375,34 +644,106 @@ std::vector<float> SignalProcessor::filter(const std::vector<float>& input,
 std::vector<float> SignalProcessor::Lowpass_filter(const std::vector<float>& input,
                                                     double sample_rate,
                                                     double low_cutoff){
-    if (input.empty()) return input;
-    std::vector<float> output(input.size());
-    double A,f;
-    f = 1/sample_rate;
-    A = 1.0f/(1+(1/(2*M_PI*f*low_cutoff)));
-    output[0] = input[0];
-    if (input.size() > 1) output[1] = input[1];
-    for (size_t n = 2; n < input.size(); n++)
-    {
-        output[n] = A*input[n] + (1-A)*output[n-1];
-    }
-    return output;
+    if (input.empty() || low_cutoff <= 0 || low_cutoff >= sample_rate/2) {
+            return input;
+        }
+
+        const double nyquist = sample_rate / 2.0;
+        const double omega = 2.0 * M_PI * low_cutoff / sample_rate;
+        const double k = 1.0 / tan(omega / 2.0);  // 双线性变换预矫正
+        const double k2 = k * k;
+        const double sqrt2 = std::sqrt(2.0);
+        
+        // 计算归一化系数
+        const double a0 = k2 + sqrt2 * k + 1;
+        const double b0 = 1.0 / a0;
+        const double b1 = 2 * b0;
+        const double b2 = b0;
+        const double a1 = 2 * (1 - k2) * b0;
+        const double a2 = (k2 - sqrt2 * k + 1) * b0;
+
+        // 应用滤波器
+        std::vector<float> output(input.size());
+        double x1 = 0.0, x2 = 0.0;  // 输入延迟
+        double y1 = 0.0, y2 = 0.0;  // 输出延迟
+
+        for (size_t i = 0; i < input.size(); ++i) {
+            const double x0 = input[i];
+            const double y0 = b0 * x0 + b1 * x1 + b2 * x2 - a1 * y1 - a2 * y2;
+            
+            output[i] = static_cast<float>(y0);
+            
+            // 更新延迟
+            x2 = x1;
+            x1 = x0;
+            y2 = y1;
+            y1 = y0;
+        }
+
+        return output;
 }
-//高通滤波器
+//改进后的高通滤波实现
 std::vector<float> SignalProcessor::Highpass_filter(const std::vector<float>& input,
                                         double sample_rate,
-                                        double high_cutoff){
+                                        double cutoff) {
     if (input.empty()) return input;
+    int a = input[0];
+    // 1. 计算滤波器系数（二阶巴特沃斯）
+    const double omega = 2.0 * M_PI * cutoff / sample_rate;
+    const double sn = sin(omega);
+    const double cs = cos(omega);
+    const double alpha = sn / (2.0 * 0.707);  // Q=0.707 (Butterworth)
+    
+    const double b0 = (1 + cs) / 2.0;
+    const double b1 = -(1 + cs);
+    const double b2 = b0;
+    const double a0 = 1 + alpha;
+    const double a1 = -2 * cs;
+    const double a2 = 1 - alpha;
+    
+    // 2. 归一化系数
+    const double inv_a0 = 1.0 / a0;
+    const double nb0 = b0 * inv_a0;
+    const double nb1 = b1 * inv_a0;
+    const double nb2 = b2 * inv_a0;
+    const double na1 = a1 * inv_a0;
+    const double na2 = a2 * inv_a0;
+    
+    // 3. 初始化状态（使用前两个样本值）
+    double x1 = input.size() > 0 ? input[0] : 0;
+    double x2 = x1;
+    double y1 = 0;
+    double y2 = 0;
+    
+    // 4. 应用滤波器（处理边界条件）
     std::vector<float> output(input.size());
-    double A,f;
-    f = 1/sample_rate;
-    A = 1.0f/(1+(1/(2*M_PI*f*high_cutoff)));
-    output[0] = input[0];
-    if (input.size() > 1) output[1] = input[1];
-    for (size_t n = 2; n < input.size(); n++)
-    {
-        output[n] = A*output[n-1] + A*(input[n]-input[n-1]);
+    for (size_t n = 0; n < input.size(); ++n) {
+        const double x0 = input[n];
+        
+        // 计算当前输出
+        double y = nb0 * x0 + nb1 * x1 + nb2 * x2 - na1 * y1 - na2 * y2;
+        
+        // 5. 稳定化处理（防止NaN/Inf）
+        if (!std::isfinite(y)) y = 0.0;
+        
+        output[n] = static_cast<float>(y);
+        
+        // 6. 更新状态变量
+        x2 = x1;
+        x1 = x0;
+        y2 = y1;
+        y1 = y;
     }
+    
+    // 7. 可选：去除初始瞬态（前100ms数据）
+    /*const size_t transient_samples = static_cast<size_t>(0.1 * sample_rate);
+    if (output.size() > transient_samples) {
+        const float initial_value = output[transient_samples];
+        for (size_t i = 0; i < transient_samples; ++i) {
+            output[i] = initial_value;
+        }
+    }*/
+    
     return output;
 }
 // 带通滤波器
@@ -454,43 +795,68 @@ std::vector<float> SignalProcessor::bandpass_filter(const std::vector<float>& in
 std::vector<float> SignalProcessor::bandstop_filter(const std::vector<float>& input, 
                                                   double sample_rate,
                                                   double low_cutoff,
-                                                  double high_cutoff) {
+                                                  double high_cutoff) 
+{
     if (input.empty()) return {};
     if (low_cutoff >= high_cutoff) {
         throw std::invalid_argument("Low cutoff must be less than high cutoff");
     }
+    if (input.size() < 4) return input;  // 太短的信号无法有效滤波
 
-    std::vector<float> output(input.size(), 0.0f);
-    const double f0 = (low_cutoff + high_cutoff) / 2.0;  // 中心频率
-    const double BW = high_cutoff - low_cutoff;          // 带宽
-    const double Q = f0 / BW;                            // 品质因数
-    const double omega0 = 2 * M_PI * f0 / sample_rate;
-    const double alpha = sin(omega0) / (2 * Q);
+    const double f0 = (low_cutoff + high_cutoff) / 2.0;
+    const double bw = high_cutoff - low_cutoff;
+    
+    // 1. 使用双线性变换进行频率预矫正
+    const double T = 1.0 / sample_rate;
+    const double w0 = 2.0 * M_PI * f0;
+    const double wd = 2.0 * M_PI * bw;
+    
+    // 预矫正模拟频率
+    const double wa = 2.0 / T * tan(w0 * T / 2.0);
+    const double Ba = 2.0 / T * tan(wd * T / 2.0);
+    
+    // 2. 计算Butterworth滤波器系数
+    const double Q = wa / Ba;  // 更精确的Q值计算
+    const double alpha = sin(w0 * T) / (2 * Q);
     
-    // 计算滤波器系数
     const double b0 = 1.0;
-    const double b1 = -2 * cos(omega0);
+    const double b1 = -2.0 * cos(w0 * T);
     const double b2 = 1.0;
     const double a0 = 1.0 + alpha;
-    const double a1 = -2 * cos(omega0);
+    const double a1 = -2.0 * cos(w0 * T);
     const double a2 = 1.0 - alpha;
 
-    // 归一化系数
-    const double inv_a0 = 1.0 / a0;
-    const double nb0 = b0 * inv_a0;
-    const double nb1 = b1 * inv_a0;
-    const double nb2 = b2 * inv_a0;
-    const double na1 = a1 * inv_a0;
-    const double na2 = a2 * inv_a0;
+    // 3. 更精确的系数归一化
+    const double gain = 1.0 / a0;  // 保证通带增益为1
+    const double nb0 = b0 * gain;
+    const double nb1 = b1 * gain;
+    const double nb2 = b2 * gain;
+    const double na1 = a1 * gain;
+    const double na2 = a2 * gain;
 
-    // 滤波器状态
-    double x1 = 0.0, x2 = 0.0;
-    double y1 = 0.0, y2 = 0.0;
+    // 4. 应用滤波器（带合理状态初始化）
+    std::vector<float> output(input.size());
+    double x1 = input[0], x2 = input[0];  // 输入状态初始化
+    double y1 = 0.0, y2 = 0.0;           // 输出状态初始化
+    
+    // 使用前两个样本计算初始输出状态
+    if (input.size() >= 2) {    
+        const double x0 = input[0];
+        y1 = nb0 * x0 + nb1 * x1 + nb2 * x2;
+        x2 = x1;
+        x1 = x0;
+    }
     
-    // 应用滤波器
     for (size_t n = 0; n < input.size(); ++n) {
         const double x0 = input[n];
-        const double y = nb0 * x0 + nb1 * x1 + nb2 * x2 - na1 * y1 - na2 * y2;
+        double y = nb0 * x0 + nb1 * x1 + nb2 * x2 - na1 * y1 - na2 * y2;
+        
+        // 5. 添加输出限幅保护
+        const double input_max = *std::max_element(input.begin(), input.end());
+        const double safety_margin = 2.0 * input_max;
+        if (std::abs(y) > safety_margin) {
+            y = std::copysign(safety_margin, y);
+        }
         
         output[n] = static_cast<float>(y);
         
@@ -500,17 +866,15 @@ std::vector<float> SignalProcessor::bandstop_filter(const std::vector<float>& in
         y2 = y1;
         y1 = y;
     }
-
     return output;
 }
-
 // 运动补偿
 std::vector<float> SignalProcessor::compensate_motion_artifact(const std::vector<float>& ppg,
                                              const std::vector<float>& motion) {
     std::vector<float> output = ppg;
     return ppg;   
 }
-// 辅助函数：计算PPG信号质量指数
+// 辅助函数：计算PPG信号质量指 数
 float SignalProcessor::calculate_PPG_sqi(const std::vector<float>& red_channel, 
                                     const std::vector<float>& ir_channel) {
     return 0.0f;
@@ -622,3 +986,26 @@ void SignalProcessor::normalize_amplitude(std::vector<float>& signal) {
         }
     }
 }
+
+// 使用示例：如何正确使用通道级滤波
+/*
+// 原来的错误用法（对单个数据包滤波）：
+std::vector<SensorData> raw_packets = get_raw_data_packets();
+for (auto& packet : raw_packets) {
+    packet = signal_processor.preprocess_signals(packet); // 错误！对单个包滤波
+}
+
+// 新的正确用法（通道级滤波）：
+std::vector<SensorData> raw_packets = get_raw_data_packets();
+std::vector<SensorData> processed_packets = 
+    signal_processor.process_channel_based_filtering(raw_packets);
+
+// 或者，如果你想保持原有的数据包结构，可以这样：
+std::vector<SensorData> raw_packets = get_raw_data_packets();
+std::vector<SensorData> processed_packets = 
+    signal_processor.process_channel_based_filtering(raw_packets);
+
+// 关键区别：
+// 1. 原来：对每个数据包内的短时间序列进行滤波（错误）
+// 2. 现在：收集所有数据包中相同通道的完整数据，进行滤波，然后重新分配
+*/