Change Parameter name from frequency to sampling_rate

src/ecg2hrv.py

@@ -8,30 +8,30 @@ class ECG2HRV(FeatureExtractor):
     def __init__(self):
         super().__init__()
 
-    def extract_features(self, ecg, frequency, baseline=None, normalization_method=None):
+    def extract_features(self, ecg, sampling_rate, baseline=None, normalization_method=None):
         # Ensure the numpy has at least one dimension (i.e. is not a scalar)
         if ecg.ndim < 1:
             raise ValueError("Array must have at least one dimension")
 
         # Preprocess the ecg signal
-        ecg = nk.ecg_clean(ecg_signal=ecg, sampling_rate=frequency, method="pantompkins1985")
+        ecg = nk.ecg_clean(ecg_signal=ecg, sampling_rate=sampling_rate, method="pantompkins1985")
 
         # Compute the HRV features
-        features = self.get_hrv_features(ecg, frequency)
+        features = self.get_hrv_features(ecg, sampling_rate)
 
         # Normalize if baseline is available and method is set - TBD
         if baseline is not None and normalization_method is not None:
-            baseline_features = self.get_hrv_features(baseline, frequency)
+            baseline_features = self.get_hrv_features(baseline, sampling_rate)
             features = self.normalize_features(features, baseline_features, normalization_method)
 
         return features
 
-    def get_hrv_features(self, ecg, frequency):
+    def get_hrv_features(self, ecg, sampling_rate):
         # Find peaks
-        peaks, info = nk.ecg_peaks(ecg, sampling_rate=frequency, method="pantompkins1985")
+        peaks, info = nk.ecg_peaks(ecg, sampling_rate=sampling_rate, method="pantompkins1985")
 
         # Compute time domain features
-        hrv_time_features = nk.hrv_time(peaks, sampling_rate=frequency)
+        hrv_time_features = nk.hrv_time(peaks, sampling_rate=sampling_rate)
         # Compute frequency domain features
         # hrv_frequency_features = nk.hrv_frequency(peaks, sampling_rate=fs, method="welch", show=False)
 

src/ecg_2_hrv_pipeline.py

@@ -6,16 +6,16 @@ class Ecg2HrvPipeline(Pipeline):
     
     def _sanitize_parameters(self, **kwargs):
         preprocess_kwargs = {}
-        if "frequency" in kwargs:
-            preprocess_kwargs["frequency"] = kwargs["frequency"]
+        if "sampling_rate" in kwargs:
+            preprocess_kwargs["sampling_rate"] = kwargs["sampling_rate"]
         if "baseline" in kwargs:
             preprocess_kwargs["baseline"] = kwargs["baseline"]
         if "normalization_method" in kwargs:
             preprocess_kwargs["normalization_method"] = kwargs["normalization_method"]
         return preprocess_kwargs, {}, {}
 
-    def preprocess(self, inputs, frequency = 100, baseline = None, normalization_method = None):
-        return self.ecg2HrvExtractor.extract_features(inputs, frequency, baseline, normalization_method)
+    def preprocess(self, inputs, sampling_rate = 1000, baseline = None, normalization_method = None):
+        return self.ecg2HrvExtractor.extract_features(inputs, sampling_rate, baseline, normalization_method)
 
     def _forward(self, model_inputs):
         # currently empty as all preprocessing steps are performed by preprocess function

src/rpeaks2hrv.py

@@ -10,21 +10,21 @@ class WindowingMethod(Enum):
 
 class RPeak2HRV():
 
-    def get_hrv_features(self, input, windowing_method:str = None, time_header = "SystemTime", rri_header = "interbeat_interval", window_size = "60s", frequency = 100):
+    def get_hrv_features(self, input, windowing_method:str = None, time_header = "SystemTime", rri_header = "interbeat_interval", window_size = "60s", sampling_rate = 1000):
         data = self._load_data(input)
-        refined_data = self._refine_dataframe(input=data, frequency=frequency, time_header=time_header, rri_header=rri_header)
+        refined_data = self._refine_dataframe(input=data, sampling_rate=sampling_rate, time_header=time_header, rri_header=rri_header)
         if (windowing_method != None):
             windows = self._apply_windowing(data=refined_data, method=windowing_method, window_size= window_size)
             # TODO: Implement hrv_time on windows
             hrv_values = pd.DataFrame(columns=['window_start', 'window_end', 'HRV_MeanNN', 'HRV_SDNN', 'HRV_SDANN1', 'HRV_SDNNI1', 'HRV_SDANN2', 'HRV_SDNNI2', 'HRV_SDANN5', 'HRV_SDNNI5', 'HRV_RMSSD', 'HRV_SDSD', 'HRV_CVNN', 'HRV_CVSD', 'HRV_MedianNN', 'HRV_MadNN', 'HRV_MCVNN', 'HRV_IQRNN', 'HRV_SDRMSSD', 'HRV_Prc20NN', 'HRV_Prc80NN', 'HRV_pNN50', 'HRV_pNN20', 'HRV_MinNN', 'HRV_MaxNN', 'HRV_HTI', 'HRV_TINN'])
             for window in windows:
-                hrv_time = nk.hrv_time(self._convert_format(window), frequency)
+                hrv_time = nk.hrv_time(self._convert_format(window), sampling_rate)
                 hrv_time['window_start'] = window.index[0]
                 hrv_time['window_end'] = window.index[-1]
                 hrv_values = pd.concat([hrv_values, hrv_time], ignore_index=True)
             return hrv_values
         else:
-            return nk.hrv_time(self._convert_format(refined_data), frequency)
+            return nk.hrv_time(self._convert_format(refined_data), sampling_rate)
     
     def _load_data(self, input):
         if isinstance(input, str):
@@ -46,11 +46,11 @@ class RPeak2HRV():
         else:
             raise ValueError('File format not supported. Please provide a csv or txt file.')
         
-    def _refine_dataframe(self, input:pd.DataFrame, time_header:str, rri_header:str, frequency = 100):
+    def _refine_dataframe(self, input:pd.DataFrame, time_header:str, rri_header:str, sampling_rate = 1000):
         input = self.__clean_data(input)
         if 'ECG_R_Peaks' in input.columns:
             # Schritt 1: Berechne die Timestamps für alle Datenpunkte
-            timestamps = pd.to_datetime(input.index / frequency, unit='s')  # Indizes durch Sampling-Rate teilen
+            timestamps = pd.to_datetime(input.index / sampling_rate, unit='s')  # Indizes durch Sampling-Rate teilen
             data = pd.DataFrame(input["ECG_R_Peaks"], columns=["ECG_R_Peaks"])
             data['Timestamp'] = timestamps
             data.set_index("Timestamp", inplace=True)

src/rpeaks_2_hrv_pipeline.py

@@ -6,8 +6,8 @@ class RPeak2HRVPipeline(Pipeline):
     
     def _sanitize_parameters(self, **kwargs):
         preprocess_kwargs = {}
-        if "frequency" in kwargs:
-            preprocess_kwargs["frequency"] = kwargs["frequency"]
+        if "sampling_rate" in kwargs:
+            preprocess_kwargs["sampling_rate"] = kwargs["sampling_rate"]
         if "windowing_method" in kwargs:
             preprocess_kwargs["windowing_method"] = kwargs["windowing_method"]
         if "time_header" in kwargs:
@@ -18,8 +18,8 @@ class RPeak2HRVPipeline(Pipeline):
             preprocess_kwargs["window_size"] = kwargs["window_size"]
         return preprocess_kwargs, {}, {}
 
-    def preprocess(self, inputs, windowing_method:str = None, time_header = "SystemTime", rri_header = "interbeat_interval", window_size = "60s", frequency = 100):
-        return self.rpeak2HRVExtractor.get_hrv_features(inputs, windowing_method=windowing_method, time_header=time_header, rri_header=rri_header, window_size=window_size, frequency=frequency)
+    def preprocess(self, inputs, windowing_method:str = None, time_header = "SystemTime", rri_header = "interbeat_interval", window_size = "60s", sampling_rate = 1000):
+        return self.rpeak2HRVExtractor.get_hrv_features(inputs, windowing_method=windowing_method, time_header=time_header, rri_header=rri_header, window_size=window_size, sampling_rate=sampling_rate)
 
     def _forward(self, model_inputs):
         # currently empty as all preprocessing steps are performed by preprocess function