diff --git a/.kotlin/sessions/kotlin-compiler-15274677907984621681.salive b/.kotlin/sessions/kotlin-compiler-15274677907984621681.salive
deleted file mode 100644
index e69de29..0000000
diff --git a/app/src/main/cpp/include/cpp/indicator_cal.h b/app/src/main/cpp/include/cpp/indicator_cal.h
index 6107f3a..0393ec1 100644
--- a/app/src/main/cpp/include/cpp/indicator_cal.h
+++ b/app/src/main/cpp/include/cpp/indicator_cal.h
@@ -42,6 +42,10 @@ public:
     float calculate_signal_quality(const std::vector<float>& signal);
     float estimate_heart_rate_from_frequency(const std::vector<float>& signal, float sample_rate);
     
+    // 频域分析相关函数
+    float calculate_snr_frequency_domain(const std::vector<float>& signal);
+    float calculate_snr_time_domain(const std::vector<float>& signal);
+    
     // 综合指标计算
     std::map<std::string, float> calculate_all_ecg_metrics(const SensorData& ecg_data, float sample_rate);
     std::map<std::string, float> calculate_all_ppg_metrics(const SensorData& ppg_data, float sample_rate);
diff --git a/app/src/main/cpp/include/cpp/signal_processor.h b/app/src/main/cpp/include/cpp/signal_processor.h
index 7580a5d..9fd1e94 100644
--- a/app/src/main/cpp/include/cpp/signal_processor.h
+++ b/app/src/main/cpp/include/cpp/signal_processor.h
@@ -10,6 +10,16 @@ enum class filtertype
 {
     lowpass,highpass,notchpass,bandpass,bandstop
 };
+
+// 一阶滤波器参数结构
+struct FirstOrderFilterParams {
+    double b0, b1;  // 分子系数
+    double a1;       // 分母系数 (a0=1)
+    
+    FirstOrderFilterParams() : b0(1.0), b1(0.0), a1(0.0) {}
+    FirstOrderFilterParams(double b0_, double b1_, double a1_) 
+        : b0(b0_), b1(b1_), a1(a1_) {}
+};
 // 特征数据结构
 struct ECGChannelFeatures {
     std::vector<int> r_peaks;     // R波位置
@@ -82,6 +92,13 @@ public:
     // 设置滤波器参数
     void setFilterParams(const std::string& filter_type, const FilterConfig& params);
     
+    // 一阶滤波器相关函数
+    FirstOrderFilterParams calculateFirstOrderFilterParams(double sample_rate, double cutoff_freq, filtertype type);
+    std::vector<float> applyFirstOrderFilter(const std::vector<float>& input, const FirstOrderFilterParams& params);
+    
+    // 一阶带通滤波器辅助函数
+    std::pair<FirstOrderFilterParams, FirstOrderFilterParams> calculateFirstOrderBandpassParams(double sample_rate, double low_cutoff, double high_cutoff);
+    std::vector<float> applyFirstOrderBandpassFilter(const std::vector<float>& input, const FirstOrderFilterParams& highpass_params, const FirstOrderFilterParams& lowpass_params);
 
     // 修改为public访问权限
     public:
diff --git a/app/src/main/cpp/src/data_praser.cpp b/app/src/main/cpp/src/data_praser.cpp
index 4aa4baf..bfe5dd4 100644
--- a/app/src/main/cpp/src/data_praser.cpp
+++ b/app/src/main/cpp/src/data_praser.cpp
@@ -311,7 +311,7 @@ SensorData parse_snore(const uint8_t* data) {
     }
 
     // 赋值原始二进制数据
-    result.raw_data.assign(data, data + 6 + 232);
+    result.raw_data.assign(data, data + 238);
 
     return result;
 }
diff --git a/app/src/main/cpp/src/indicator_cal.cpp b/app/src/main/cpp/src/indicator_cal.cpp
index a4de769..2d1ed0d 100644
--- a/app/src/main/cpp/src/indicator_cal.cpp
+++ b/app/src/main/cpp/src/indicator_cal.cpp
@@ -979,38 +979,37 @@ std::vector<float> MetricsCalculator::detect_pulse_peaks(const std::vector<float
     return pulse_peaks;
 }
 
-// 新增：信号质量评估
+// 新增：信号质量评估（基于频域分析）
 float MetricsCalculator::calculate_signal_quality(const std::vector<float>& signal) {
     if (signal.empty()) return 0.0f;
     
     const size_t n = signal.size();
     
-    // 1. 计算信噪比 (SNR)
-    float signal_power = 0.0f;
-    float noise_power = 0.0f;
-    
-    // 使用简化的功率谱密度计算（避免复杂的FFT）
-    // 假设低频部分是信号，高频部分是噪声
-    size_t mid_point = n / 2;
-    for (size_t i = 0; i < mid_point; i++) {
-        signal_power += signal[i] * signal[i];
-    }
-    for (size_t i = mid_point; i < n; i++) {
-        noise_power += signal[i] * signal[i];
-    }
-    
-    float snr = 0.0f;
-    if (noise_power > 0.0001f) {
-        snr = 10.0f * std::log10(signal_power / noise_power);
-    }
+    // 1. 计算信噪比 (SNR) - 使用频域分析
+    float snr = calculate_snr_frequency_domain(signal);
     
     // 2. 计算信号连续性
     float continuity_score = 0.0f;
     int discontinuity_count = 0;
     
+    // 计算信号的标准差作为自适应阈值的基础
+    float mean = 0.0f;
+    for (float val : signal) {
+        mean += val;
+    }
+    mean /= n;
+    
+    float variance = 0.0f;
+    for (float val : signal) {
+        float diff = val - mean;
+        variance += diff * diff;
+    }
+    variance /= n;
+    float std_dev = std::sqrt(variance);
+    
     for (size_t i = 1; i < n; i++) {
         float diff = std::abs(signal[i] - signal[i-1]);
-        if (diff > 10.0f * std::sqrt(noise_power / n)) { // 自适应阈值
+        if (diff > 3.0f * std_dev) { // 使用3倍标准差作为阈值
             discontinuity_count++;
         }
     }
@@ -1199,4 +1198,174 @@ std::map<std::string, float> MetricsCalculator::calculate_all_hrv_metrics(const
     metrics["total_power"] = total_power;
     
     return metrics;
+}
+
+// ============================================================================
+// 频域分析相关函数
+
+// 简单的FFT实现（使用Cooley-Tukey算法）
+std::vector<std::complex<float>> fft(const std::vector<float>& signal) {
+    const size_t n = signal.size();
+    
+    // 确保信号长度是2的幂次
+    size_t padded_size = 1;
+    while (padded_size < n) {
+        padded_size <<= 1;
+    }
+    
+    std::vector<std::complex<float>> padded_signal(padded_size, 0.0f);
+    for (size_t i = 0; i < n; i++) {
+        padded_signal[i] = std::complex<float>(signal[i], 0.0f);
+    }
+    
+    // Cooley-Tukey FFT
+    std::vector<std::complex<float>> result = padded_signal;
+    
+    // 位反转
+    for (size_t i = 1, j = 0; i < padded_size; i++) {
+        size_t bit = padded_size >> 1;
+        while (j & bit) {
+            j ^= bit;
+            bit >>= 1;
+        }
+        j ^= bit;
+        if (i < j) {
+            std::swap(result[i], result[j]);
+        }
+    }
+    
+    // FFT计算
+    for (size_t len = 2; len <= padded_size; len <<= 1) {
+        float angle = -2.0f * M_PI / len;
+        std::complex<float> wlen(std::cos(angle), std::sin(angle));
+        
+        for (size_t i = 0; i < padded_size; i += len) {
+            std::complex<float> w(1.0f, 0.0f);
+            for (size_t j = 0; j < len / 2; j++) {
+                std::complex<float> u = result[i + j];
+                std::complex<float> v = result[i + j + len / 2] * w;
+                result[i + j] = u + v;
+                result[i + j + len / 2] = u - v;
+                w *= wlen;
+            }
+        }
+    }
+    
+    return result;
+}
+
+// 计算功率谱密度
+std::vector<float> calculate_power_spectrum(const std::vector<std::complex<float>>& fft_result) {
+    std::vector<float> power_spectrum(fft_result.size() / 2);
+    
+    for (size_t i = 0; i < power_spectrum.size(); i++) {
+        float magnitude = std::abs(fft_result[i]);
+        power_spectrum[i] = magnitude * magnitude;
+    }
+    
+    return power_spectrum;
+}
+
+// 计算指定频带的功率
+float calculate_power_in_frequency_band(const std::vector<float>& power_spectrum, 
+                                       float sample_rate, 
+                                       float low_freq, 
+                                       float high_freq) {
+    const size_t n = power_spectrum.size();
+    const float freq_resolution = sample_rate / (2.0f * n);
+    
+    size_t low_bin = static_cast<size_t>(low_freq / freq_resolution);
+    size_t high_bin = static_cast<size_t>(high_freq / freq_resolution);
+    
+    // 确保索引在有效范围内
+    low_bin = std::min(low_bin, n - 1);
+    high_bin = std::min(high_bin, n - 1);
+    
+    if (low_bin >= high_bin) return 0.0f;
+    
+    float power = 0.0f;
+    for (size_t i = low_bin; i <= high_bin; i++) {
+        power += power_spectrum[i];
+    }
+    
+    return power;
+}
+
+// 基于频域分析计算信噪比
+float MetricsCalculator::calculate_snr_frequency_domain(const std::vector<float>& signal) {
+    if (signal.size() < 64) {
+        // 信号太短，使用简化的时域方法
+        return calculate_snr_time_domain(signal);
+    }
+    
+    // 假设采样率为250Hz（可以根据实际情况调整）
+    const float sample_rate = 250.0f;
+    
+    // 进行FFT变换
+    auto fft_result = fft(signal);
+    
+    // 计算功率谱
+    auto power_spectrum = calculate_power_spectrum(fft_result);
+    
+    // 定义生物电信号的频带（Hz）
+    const float signal_low = 0.5f;    // 生物电信号低频下限
+    const float signal_high = 30.0f;   // 生物电信号高频上限
+    const float noise_low = 50.0f;     // 噪声频带下限
+    const float noise_high = 125.0f;   // 噪声频带上限（奈奎斯特频率的一半）
+    
+    // 计算信号频带功率
+    float signal_power = calculate_power_in_frequency_band(power_spectrum, sample_rate, 
+                                                          signal_low, signal_high);
+    
+    // 计算噪声频带功率
+    float noise_power = calculate_power_in_frequency_band(power_spectrum, sample_rate, 
+                                                         noise_low, noise_high);
+    
+    // 如果噪声功率太小，使用总功率的10%作为噪声估计
+    if (noise_power < signal_power * 0.01f) {
+        float total_power = 0.0f;
+        for (float p : power_spectrum) {
+            total_power += p;
+        }
+        noise_power = total_power * 0.1f;
+    }
+    
+    // 计算SNR (dB)
+    float snr = 0.0f;
+    if (noise_power > 0.0001f) {
+        snr = 10.0f * std::log10(signal_power / noise_power);
+    }
+    
+    return snr;
+}
+
+// 简化的时域SNR计算（用于短信号）
+float MetricsCalculator::calculate_snr_time_domain(const std::vector<float>& signal) {
+    if (signal.empty()) return 0.0f;
+    
+    // 计算信号均值
+    float mean = 0.0f;
+    for (float val : signal) {
+        mean += val;
+    }
+    mean /= signal.size();
+    
+    // 计算信号功率（去直流分量）
+    float signal_power = 0.0f;
+    for (float val : signal) {
+        float centered = val - mean;
+        signal_power += centered * centered;
+    }
+    signal_power /= signal.size();
+    
+    // 使用信号功率的10%作为噪声估计
+    float noise_power = signal_power * 0.1f;
+    
+    // 计算SNR
+    float snr = 0.0f;
+    if (noise_power > 0.0001f) {
+        snr = 10.0f * std::log10(signal_power / noise_power);
+    }
+    
+    return snr;
 }
\ No newline at end of file
diff --git a/app/src/main/cpp/src/signal_processor.cpp b/app/src/main/cpp/src/signal_processor.cpp
index 3362222..d322104 100644
--- a/app/src/main/cpp/src/signal_processor.cpp
+++ b/app/src/main/cpp/src/signal_processor.cpp
@@ -12,157 +12,6 @@ T clamp(T value, T min_val, T max_val) {
     return value;
 }
 
-// 新增：简化的通道级滤波处理（测试版本）
-std::vector<SensorData> SignalProcessor::process_channel_based_filtering_simple(
-    const std::vector<SensorData>& data_packets) {
-    
-    std::cout << "=== 开始简化版通道级滤波处理 ===" << std::endl;
-    
-    if (data_packets.empty()) {
-        std::cout << "输入数据包为空，返回空结果" << std::endl;
-        return {};
-    }
-    
-    std::cout << "输入数据包数量: " << data_packets.size() << std::endl;
-    
-    // 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::cout << "按数据类型分组完成，共 " << grouped_data.size() << " 种类型" << std::endl;
-    
-    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;
-        
-        std::cout << "处理数据类型: " << static_cast<int>(data_type) << "，数据包数量: " << packets.size() << std::endl;
-        
-        // 获取第一个数据包作为模板
-        SensorData template_packet = packets[0];
-        
-        // 检查通道数据类型
-        if (!std::holds_alternative<std::vector<std::vector<float>>>(packets[0].channel_data)) {
-            std::cout << "警告: 数据类型 " << static_cast<int>(data_type) << " 不是多通道格式，跳过" << std::endl;
-            continue;
-        }
-        
-        // 获取通道信息
-        auto& first_channels = std::get<std::vector<std::vector<float>>>(packets[0].channel_data);
-        size_t num_channels = first_channels.size();
-        size_t samples_per_packet = first_channels.empty() ? 0 : first_channels[0].size();
-        
-        std::cout << "通道数量: " << num_channels << ", 每包采样点数: " << samples_per_packet << std::endl;
-        
-        if (num_channels == 0 || samples_per_packet == 0) {
-            std::cout << "警告: 通道数据无效，跳过此类型" << std::endl;
-            continue;
-        }
-        
-        // 计算总采样点数
-        size_t total_samples = samples_per_packet * packets.size();
-        std::cout << "总采样点数: " << total_samples << std::endl;
-        
-        // 创建合并后的数据包
-        SensorData merged_packet = template_packet;
-        merged_packet.packet_sn = 0; // 合并后的包序号为0
-        
-        // 创建合并后的通道数据
-        auto& merged_channels = merged_packet.channel_data.emplace<std::vector<std::vector<float>>>();
-        merged_channels.resize(num_channels);
-        
-        // 合并所有数据包的通道数据
-        for (size_t ch = 0; ch < num_channels; ch++) {
-            merged_channels[ch].reserve(total_samples);
-            
-            for (const auto& packet : packets) {
-                if (auto* channels = std::get_if<std::vector<std::vector<float>>>(&packet.channel_data)) {
-                    if (ch < channels->size() && !(*channels)[ch].empty()) {
-                        merged_channels[ch].insert(merged_channels[ch].end(),
-                                                  (*channels)[ch].begin(),
-                                                  (*channels)[ch].end());
-                    }
-                }
-            }
-            
-            std::cout << "通道 " << ch << " 合并完成，采样点数: " << merged_channels[ch].size() << std::endl;
-        }
-        
-        // 对合并后的数据进行基本信号处理
-        std::cout << "开始基本信号处理..." << std::endl;
-        for (size_t ch = 0; ch < num_channels; ch++) {
-            if (merged_channels[ch].empty()) continue;
-            
-            try {
-                // 根据数据类型应用不同的基本处理
-                switch (data_type) {
-                    case DataType::ECG_2LEAD:
-                        // ECG 2导联基本处理：去除直流分量
-                        merged_channels[ch] = remove_dc_offset(merged_channels[ch]);
-                        break;
-                    case DataType::ECG_12LEAD:
-                        // ECG 12导联专业滤波处理
-                        std::cout << "通道 " << ch << " 开始12导联心电专业滤波..." << std::endl;
-                        
-                        // 1. 去除直流分量
-                        merged_channels[ch] = remove_dc_offset(merged_channels[ch]);
-                        std::cout << "   - 直流分量去除完成" << std::endl;
-                        
-                        // 2. 0.1Hz高通滤波（去除基线漂移，比0.5Hz更温和）
-                        merged_channels[ch] = filter(merged_channels[ch], 250.0, 0, 0.1, filtertype::highpass);
-                        std::cout << "   - 0.1Hz高通滤波完成" << std::endl;
-                        
-                        // 3. 50Hz陷波滤波（去除工频干扰，带宽1.0Hz更精确）
-                        merged_channels[ch] = filter(merged_channels[ch], 250.0, 49.5, 50.5, filtertype::notchpass);
-                        std::cout << "   - 50Hz陷波滤波完成" << std::endl;
-                        
-                        std::cout << "通道 " << ch << " 12导联心电滤波处理完成" << std::endl;
-                        break;
-                    case DataType::EEG:
-                        // EEG基本处理：去除直流分量
-                        merged_channels[ch] = remove_dc_offset(merged_channels[ch]);
-                        break;
-                    case DataType::PPG:
-                        // PPG基本处理：去除直流分量
-                        merged_channels[ch] = remove_dc_offset(merged_channels[ch]);
-                        // 2. 0.5-8Hz带通滤波（更精确的PPG频带）
-                         merged_channels[ch] = bandpass_filter(merged_channels[ch], 50, 0.5, 8.0);
-                        // // 3. 50Hz陷波滤波（去除工频干扰）
-                         //merged_channels[ch] = filter(merged_channels[ch], 50, 49.5, 50.5, filtertype::notchpass);
-                        // // 4. 运动伪迹检测和去除（优化版本）
-                         merged_channels[ch] = remove_motion_artifacts(merged_channels[ch], 50);
-                        break;
-                    case DataType::RESPIRATION:
-                        // 呼吸信号基本处理：去除直流分量
-                        merged_channels[ch] = remove_dc_offset(merged_channels[ch]);
-                        break;
-                    default:
-                        // 通用处理：去除直流分量
-                        merged_channels[ch] = remove_dc_offset(merged_channels[ch]);
-                        break;
-                }
-                
-                std::cout << "通道 " << ch << " 基本处理完成" << std::endl;
-            } catch (const std::exception& e) {
-                std::cerr << "通道 " << ch << " 处理失败: " << e.what() << std::endl;
-                // 继续处理其他通道
-            }
-        }
-        
-        // 添加到处理结果中
-        processed_packets.push_back(merged_packet);
-        
-        std::cout << "数据类型 " << static_cast<int>(data_type) << " 处理完成" << std::endl;
-    }
-    
-    std::cout << "=== 简化版通道级滤波处理完成 ===" << std::endl;
-    std::cout << "总共创建 " << processed_packets.size() << " 个合并数据包" << std::endl;
-    
-    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(
@@ -1688,3 +1537,87 @@ std::vector<float> SignalProcessor::apply_smoothing(
     return smoothed_signal;
 }
 
+// ============================================================================
+// 一阶滤波器实现
+
+// 计算一阶滤波器参数
+FirstOrderFilterParams SignalProcessor::calculateFirstOrderFilterParams(double sample_rate, double cutoff_freq, filtertype type) {
+    if (sample_rate <= 0 || cutoff_freq <= 0 || cutoff_freq >= sample_rate / 2) {
+        // 返回单位滤波器参数（不滤波）
+        return FirstOrderFilterParams(1.0, 0.0, 0.0);
+    }
+    //α = exp(-ωc * T) = exp(-2π * fc / fs)
+    // 计算归一化频率
+    double omega = 2.0 * M_PI * cutoff_freq / sample_rate;
+    double alpha = std::exp(-omega);
+    switch (type) {
+        case filtertype::lowpass: {
+            return FirstOrderFilterParams(1.0 - alpha, 0.0, -alpha);
+        }
+        
+        case filtertype::highpass: {  // y[n] = b0*x[n] + b1*x[n-1] - a1*y[n-1]
+            double gain = (1.0 + alpha) / 2.0;
+            return FirstOrderFilterParams(gain, -gain, -alpha);
+        }
+        
+        case filtertype::bandpass: {
+            double gain = (1.0 + alpha) / 2.0;
+            return FirstOrderFilterParams(gain, -gain, -alpha);
+        }
+
+        default:
+            return FirstOrderFilterParams(1.0, 0.0, 0.0);
+    }
+}
+
+// 应用一阶滤波器
+std::vector<float> SignalProcessor::applyFirstOrderFilter(const std::vector<float>& input, const FirstOrderFilterParams& params) {
+    if (input.empty()) {
+        return input;
+    }
+    
+    std::vector<float> output(input.size());
+    
+    // 初始化状态变量
+    double x1 = 0.0;  // 输入延迟
+    double y1 = 0.0;  // 输出延迟
+    // 应用差分方程：y[n] = b0*x[n] + b1*x[n-1] - a1*y[n-1]
+    for (size_t i = 0; i < input.size(); i++) {
+        double x0 = input[i];
+        double y0 = params.b0 * x0 + params.b1 * x1 - params.a1 * y1;
+        
+        output[i] = static_cast<float>(y0);
+        
+        // 更新延迟
+        x1 = x0;
+        y1 = y0;
+    }
+    
+    return output;
+}
+
+// 辅助函数：计算一阶带通滤波器参数（高通 + 低通）
+std::pair<FirstOrderFilterParams, FirstOrderFilterParams> 
+SignalProcessor::calculateFirstOrderBandpassParams(double sample_rate, double low_cutoff, double high_cutoff) {
+    // 高通滤波器参数
+    FirstOrderFilterParams highpass_params = calculateFirstOrderFilterParams(sample_rate, low_cutoff, filtertype::highpass);
+    
+    // 低通滤波器参数
+    FirstOrderFilterParams lowpass_params = calculateFirstOrderFilterParams(sample_rate, high_cutoff, filtertype::lowpass);
+    
+    return std::make_pair(highpass_params, lowpass_params);
+}
+
+// 应用一阶带通滤波器（级联高通和低通）
+std::vector<float> SignalProcessor::applyFirstOrderBandpassFilter(const std::vector<float>& input, 
+                                                                  const FirstOrderFilterParams& highpass_params,
+                                                                  const FirstOrderFilterParams& lowpass_params) {
+    // 先应用高通滤波
+    std::vector<float> highpass_output = applyFirstOrderFilter(input, highpass_params);
+    
+    // 再应用低通滤波
+    std::vector<float> bandpass_output = applyFirstOrderFilter(highpass_output, lowpass_params);
+    
+    return bandpass_output;
+}
+