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Fix HRV frequency PSD scale and Systole heart_rate API #1

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Fix HRV frequency PSD scale and Systole heart_rate API #1

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24 changes: 15 additions & 9 deletions code/hrv/hrv_analysis.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -230,7 +230,7 @@
" - physio_type (str, optional): If 'bbsig_preproc=True', type of physiological signal used. Default is 'ecg', can also be 'ppg'.\n",
"\n",
" Returns: \n",
" - A dictionary with two keys: 'RRI' (array of RR intervals in ms); 'RRI_Time' (array of cumulative R-peaks timestamps in ms). \n",
" - A dictionary with two keys: 'RRI' (array of RR intervals in ms); 'RRI_Time' (array of cumulative R-peaks timestamps in seconds). \n",
" \"\"\"\n",
"\n",
" ############## Data loading: BBSIG preprocessing files ##############\n",
Expand Down Expand Up @@ -288,15 +288,18 @@
"\n",
" # Convert RR intervals from seconds to milliseconds, if necessary\n",
" if rri_unit == 's':\n",
" rr_s = rr_s*1000 \n",
" \n",
" rr_ms = rr_s * 1000\n",
" else:\n",
" rr_ms = rr_s\n",
"\n",
" # Convert data into appropriate formats for NeuroKit2\n",
" peaks = np.cumsum(rr_s) # define R-peaks as the cumulative sum of RR intervals\n",
" # NeuroKit2 expects RRI in milliseconds and RRI_Time in seconds.\n",
" peaks_s = np.cumsum(rr_ms) / 1000 # R-peak timestamps in seconds\n",
"\n",
" # Store data for participant in a dictionary\n",
" rrs_dict = {\n",
" 'RRI': np.array(rr_s), # RR intervals in miliseconds\n",
" 'RRI_Time': peaks} # R-peaks timestamps in seconds \n",
" 'RRI': np.array(rr_ms), # RR intervals in milliseconds\n",
" 'RRI_Time': peaks_s} # R-peaks timestamps in seconds\n",
" \n",
" return rrs_dict"
]
Expand Down Expand Up @@ -324,7 +327,7 @@
"def hrv_window_crop(rrs_dict, window_start=0, window_end=None, window_length=None):\n",
" \"\"\" Crop RR interval time series to a specified time window for HRV analysis.\n",
" Parameters:\n",
" - rrs_dict (dict): Dictionary storing RR intervals in ms R-peaks timestamps in ms, generated by load_rr_data().\n",
" - rrs_dict (dict): Dictionary storing RR intervals in ms and R-peaks timestamps in seconds, generated by load_rr_data().\n",
" - window_start (float): Start time of the window to be cropped (in seconds).\n",
" - window_end (float, optional): End time of the window to be cropped (in seconds).\n",
" - window_length (float, optional): Length of the window to be cropped (in seconds).\n",
Expand Down Expand Up @@ -355,7 +358,7 @@
"\n",
" # Selected the R-peaks comprised between window_start and window_end\n",
" rrs_dict['RRI_Time'] = rrs_dict['RRI_Time'].tolist()\n",
" rrt_cropped = [x for x in rrs_dict['RRI_Time'] if x>=window_start*1000 and x<=window_end*1000]\n",
" rrt_cropped = [x for x in rrs_dict['RRI_Time'] if x >= window_start and x <= window_end]\n",
"\n",
" # Get indices of window_start and window_end and extract RR interval durations comprised in-between\n",
" window_start_idx = rrs_dict['RRI_Time'].index(rrt_cropped[0])\n",
Expand Down Expand Up @@ -457,7 +460,10 @@
" # Calculate Frequency-domain HRV metrics using NeuroKit2\n",
" # Using Welch method and interpolation at 4Hz by default\n",
" # If plot is needed, set show=True\n",
" hrv_f = nk.hrv_frequency(rrs_dict, interpolation_rate=interpolation_freq, \n",
" # ulf=(0, 0) disables the ULF band, which otherwise returns spurious values\n",
" # for short (<5 min) recordings and dominates the PSD plot.\n",
" hrv_f = nk.hrv_frequency(rrs_dict, interpolation_rate=interpolation_freq,\n",
" ulf=(0, 0),\n",
" normalize=False, psd_method='welch', show=show_plots) \n",
" \n",
" # Extract and format metrics\n",
Expand Down
2 changes: 1 addition & 1 deletion code/preprocessing/ecg_preproc.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -929,7 +929,7 @@
"\n",
" # Interpolate HR values using Systole heart_rate()\n",
" # Hot fix @April 3rd, 2025: removed sfreq=sfreq from arguments due to bug with Systole mis-calculating HR (until it will be fixed)\n",
" hr_bpm, hr_time = heart_rate(x=rr_tmp, unit='bpm', kind=interpol_type, input_type='rr_s') \n",
" hr_bpm, hr_time = heart_rate(x=rr_tmp, output_unit='bpm', kind=interpol_type, input_type='rr_s') \n",
"\n",
" # Define directory for TSV file with interpolated HR values\n",
" hr_tsv_fname = f'{bids_base_fname}_hr-bpm-{rr_type}.tsv.gz'\n",
Expand Down