U g{jr@sdZddlZddlZddlmZddlmZmZddl m Z m Z m Z m Z ddlmZddlZdd d d d d dgZifddZifdd Zdd Zd dd Ziifdd Zdddiidfdd ZddddgiifddZgiifddZdS)!za Functions that handle computation of heart rate (HR) and heart rate variability (HRV) measures. N)UnivariateSpline)welch periodogram)MAD rolling_meanoutliers_iqr_methodoutliers_modified_z)quotient_filtercalc_rr update_rrcalc_rr_segmentclean_rr_intervalscalc_ts_measurescalc_fd_measurescalc_breathingcsttdkrRd|ddkrRtd|d<t|dd|d<t|d}fddttdD}tt|}t|d }||d <||d <||d <||d <|S)acalculates peak-peak intervals Function that calculates the peak-peak data required for further analysis. Stores results in the working_data{} dict. Parameters ---------- peaklist : 1d list or array list or array containing detected peak positions sample_rate : int or float the sample rate with which the heart rate signal is collected working_data : dict dictionary object that contains all heartpy's working data (temp) objects. will be created if not passed to function Returns ------- working_data : dict working_data dictionary object containing all of heartpy's temp objects Examples -------- Let's assume we detected peaks at these positions in the signal: >>> peaklist = [200, 280, 405, 501, 615] It is then easy to call calc_rr to compute what we need: >>> wd = calc_rr(peaklist, sample_rate = 100.0) >>> wd['RR_list'] array([ 800., 1250., 960., 1140.]) >>> wd['RR_diff'] array([450., 290., 180.]) >>> wd['RR_sqdiff'] array([202500., 84100., 32400.]) Note that the list of peak-peak intervals is of length len(peaks) - 1 the length of the differences is of length len(peaks) - 2 r@@peaklistybeatcs g|]}||dfqS)r.0irrO/var/www/html/bwave/python/venv/lib/python3.8/site-packages/heartpy/analysis.py Nszcalc_rr..rRR_listZ RR_indicesRR_diff RR_sqdiff)nparraylendeletediffrangeabspower)r sample_rate working_datarr_listZ rr_indicesrr_diff rr_sqdiffrrrr s*    cs|d|dfddttD}fddttD}tjj|d}tt|}||j}t|d}||d<||d <||d <||d <|S) aupdates differences between adjacent peak-peak distances Function that updates RR differences and RR squared differences based on corrected RR list Parameters ---------- working_data : dict dictionary object that contains all heartpy's working data (temp) objects will be created if not passed to function Returns ------- out : dict working_data dictionary object containing all of heartpy's temp objects Examples -------- Let's assume we detected peaks at these positions in the signal: >>> peaklist = [200, 280, 405, 410, 501, 615] And we subsequently ran further analysis: >>> wd = calc_rr(peaklist, sample_rate = 100.0) The peak at position 410 is likely an incorrect detection and will be marked as such by other heartpy functions. This is indicated by an array 'binary_peaklist' in working_data. Binary peaklist is of the same length as peaklist, and is formatted as a mask: For now let's set it manually, normally this is done by the check_peaks() function from HeartPy's peakdetection module. >>> wd['binary_peaklist'] = [1, 1, 1, 0, 1, 1] Rejected peaks are marked with a zero and accepted with a 1. By now running update_rr(), heartpy will update all associated measures and will only compute peak-peak intervals between two accepted peaks. >>> wd = update_rr(wd) This will have generated a corrected RR_list object in the dictionary: >>> wd['RR_list_cor'] [800.0, 1250.0, 1140.0] As well as updated the lists RR_diff (differences between adjacent peak-peak intervals) and RR_sqdiff (squared differences between adjacent peak-peak intervals). rZbinary_peaklistcs,g|]$}||ddkr|qSrrrr b_peaklist rr_sourcerrrszupdate_rr..cs,g|]$}||ddkr$dndqSrrrrrr0rrrsmaskr RR_masklist RR_list_corrr r&r#r!mar"r'r%r5r()r*r+rr_mask rr_maskedr,r-rr/rr Xs4  csxfddttD}fddttD}tjj|d}tt|}||j}t|d}|||fS)acalculates peak-peak differences for segmentwise processing Function that calculates rr-measures when analysing segmentwise in the 'fast' mode. Parameters ---------- rr_source : 1d list or array list or array containing peak-peak intervals. b_peaklist : 1d list or array list or array containing mask for peaklist. Returns ------- rr_list : array array containing peak-peak intervals. rr_diff : array array containing differences between adjacent peak-peak intervals rr_sqdiff : array array containing squared differences between adjacent peak-peak intervals Examples -------- The function works in the same way as update_rr, except it returns three separate objects. It's used by process_segmentwise. Revert to doc on update_rr for more information. >>> rr, rrd, rrsd = calc_rr_segment(rr_source = [ 800., 1250., 50., 910., 1140., 1002., 1142.], ... b_peaklist = [1, 1, 1, 0, 1, 1, 1, 1]) >>> print(rr) [800.0, 1250.0, 1140.0, 1002.0, 1142.0] >>> print(rrd) [450.0 138.0 140.0] >>> print(rrsd) [202500. 19044. 19600.] cs,g|]$}||ddkr|qSr.rrr/rrrsz#calc_rr_segment..cs,g|]$}||ddkr$dndqSr2rrr3rrrsr4rr8)r1r0r+r:r;r,r-rr/rr s(  quotient-filtercsfddttdD}|dkrZtd\}}d}|D]}d|||<qFn||dkrtd\}}d}|D]}d|||<qn@|dkrtd d}d dtd |D}ntd tj j d |d }t t |}||j }t|d } |d<t|d<|d<| d<zdddD} dddD} d} d} tt|D]>}||dkrz| || krz| | || | |qzt| d<t| d<Wn YnXS)adetects and rejects outliers in peak-peak intervals Function that detects and rejects outliers in the peak-peak intervals. It updates the RR_list_cor in the working data dict Parameters ---------- working_data : dict dictionary object that contains all heartpy's working data (temp) objects. Needs to contain RR_list_cor, meaning one analysis cycle has already completed. method : str which method to use for outlier rejection, included are: - 'quotient-filter', based on the work in "Piskorki, J., Guzik, P. (2005), Filtering Poincare plots", - 'iqr', which uses the inter-quartile range, - 'z-score', which uses the modified z-score method. default : quotient-filter Returns ------- working_data : dict dictionary object that contains all heartpy's working data (temp) objects. will be created if not passed to function Examples -------- Let's load some data >>> import heartpy as hp >>> data, timer = hp.load_exampledata(1) >>> sample_rate = hp.get_samplerate_mstimer(timer) Run at least one analysis cycle first so that the dicts are populated >>> wd, m = hp.process(data, sample_rate) >>> wd = clean_rr_intervals(working_data = wd) >>> ['%.3f' %x for x in wd['RR_list_cor'][0:5]] ['897.470', '811.997', '829.091', '777.807', '803.449'] You can also specify the outlier rejection method to be used, for example using the z-score method: >>> wd = clean_rr_intervals(working_data = wd, method = 'z-score') >>> ['%.3f' %x for x in wd['RR_list_cor'][0:5]] ['897.470', '811.997', '829.091', '777.807', '803.449'] Or the inter-quartile range (iqr) based method: >>> wd = clean_rr_intervals(working_data = wd, method = 'iqr') >>> ['%.3f' %x for x in wd['RR_list_cor'][0:5]] ['897.470', '811.997', '829.091', '965.849', '803.449'] cs g|]}d|dkr|qS)r6rrrr*rrrsz&clean_rr_intervals..r6Ziqrr7rzz-scorer<rcSsg|]\}}|dkr|qS)rr)rxyrrrrsz_Incorrect method specified, use either "iqr", "z-score" or "quotient-filtering". Nothing to do!r4rZ RR_maskedrr cSsg|]}|qSrrrr>rrrr)s removed_beatscSsg|]}|qSrrr@rrrr*sremoved_beats_yrr)r&r#lowerrr r zip ValueErrorr!r9r"r'r%r5r(asarrayappend)r*methodZRR_cor_indicesZ rr_cleanedZreplaced_indicesr:rr;r,r-rArBrrrr=rrsJ7     cCsdt||d<t||d<t||d<t||d<tt||d<|t|dk}|t|dk}||d <||d <z tt|tt||d <Wntj|d <YnXz tt|tt||d <Wntj|d <YnXt||d <||fS)acalculates standard time-series measurements. Function that calculates the time-series measurements for HeartPy. Parameters ---------- rr_list : 1d list or array list or array containing peak-peak intervals rr_diff : 1d list or array list or array containing differences between adjacent peak-peak intervals rr_sqdiff : 1d list or array squared rr_diff measures : dict dictionary object used by heartpy to store computed measures. Will be created if not passed to function. working_data : dict dictionary object that contains all heartpy's working data (temp) objects. will be created if not passed to function Returns ------- working_data : dict dictionary object that contains all heartpy's working data (temp) objects. measures : dict dictionary object used by heartpy to store computed measures. Examples -------- Normally this function is called during the process pipeline of HeartPy. It can of course also be used separately. Assuming we have the following peak-peak distances: >>> import numpy as np >>> rr_list = [1020.0, 990.0, 960.0, 1000.0, 1050.0, 1090.0, 990.0, 900.0, 900.0, 950.0, 1080.0] we can then compute the other two required lists by hand for now: >>> rr_diff = np.diff(rr_list) >>> rr_sqdiff = np.power(rr_diff, 2) >>> wd, m = calc_ts_measures(rr_list, rr_diff, rr_sqdiff) All output measures are then accessible from the measures object through their respective keys: >>> print('%.3f' %m['bpm']) 60.384 >>> print('%.3f' %m['rmssd']) 67.082 i`ZbpmZibiZsdnnZsdsdZrmssdg4@gI@nn20nn50Zpnn20Zpnn50Zhr_mad) r!meanZstdsqrtwherefloatr#nanr)r+r,r-measuresr*rIrJrrrr;s&9   rFcCs|d}t|dkrXtj|d<tj|d<tj|d<tj|d<tj|d<tj|d<||fSt|d kr|d d }t|tt|}d } t t|d| } t t |d t |d| } t||krt |||d} | | } t |d}d|}|| }|dkrbtj j| d|d}|tt | d}tj | | }|tt | d}t|d}n`|dkr~t| |d\}}nD|dkr||}|t| krt| }t| ||d\}}ntd||d<|rt|d|d<n||d<|d|d }tjt||dk|dk@|d|d<tjt||dk|dk@|d|d<tjt||dk|dk@|d|d<|d|d|d<|d|d|d|d<d|d}|d||d <|d||d!<|d||d"<d|d|d}|d||d#<|d||d$<| |d%<| |d&<ntj|d<tj|d<tj|d<tj|d<tj|d<tj|d<tj|d<tj|d <tj|d!<tj|d"<tj|d#<tj|d$<tj|d%<| |d&<||fS)'acalculates the frequency-domain measurements. Function that calculates the frequency-domain measurements for HeartPy. Parameters ---------- method : str method used to compute the spectrogram of the heart rate. available methods: fft, periodogram, and welch default : welch welch_wsize : float Size of window (in sec) when welch method used to compute the spectrogram. This choice is based on a trade-off btw temporal res and freq res of the resulting spectrum 60 sec may seem reasonable, but this would greatly limit frequency resolution! 1/60 s = 0.017 Hz, so you would only have 2 points in the VLF band Therefore, the default is 4 min (9 points in the VLF band) default : 240 square_spectrum : bool whether to square the power spectrum returned. default : False measures : dict dictionary object used by heartpy to store computed measures. Will be created if not passed to function. working_data : dict dictionary object that contains all heartpy's working data (temp) objects. will be created if not passed to function degree_smoothing_spline : int, optional Degree of the smoothing spline for UnivariateSpline function in fitpack2. Must be 1 <= `k` <= 5. Default is `k` = 3, a cubic spline. Returns ------- working_data : dict dictionary object that contains all heartpy's working data (temp) objects. measures : dict dictionary object used by heartpy to store computed measures. Examples -------- Normally this function is called during the process pipeline of HeartPy. It can of course also be used separately. Let's load an example and get a list of peak-peak intervals >>> import heartpy as hp >>> data, timer = hp.load_exampledata(2) >>> sample_rate = hp.get_samplerate_datetime(timer, timeformat='%Y-%m-%d %H:%M:%S.%f') >>> wd, m = hp.process(data, sample_rate) wd now contains a list of peak-peak intervals that has been cleaned of outliers ('RR_list_cor'). Calling the function then is easy >>> wd, m = calc_fd_measures(method = 'periodogram', measures = m, working_data = wd) >>> print('%.3f' %m['lf/hf']) 5.198 Available methods are 'fft', 'welch' and 'periodogram'. To set another method, do: >>> wd, m = calc_fd_measures(method = 'fft', measures = m, working_data = wd) >>> print('%.3f' %m['lf/hf']) 5.198 If there are no valid peak-peak intervals specified, returned measures are NaN: >>> wd['RR_list_cor'] = [] >>> wd, m = calc_fd_measures(working_data = wd) >>> np.isnan(m['lf/hf']) True If there are rr-intervals but not enough to reliably compute frequency measures, a warning is raised: -------------- RuntimeWarning: Short signal. ---------Warning:--------- too few peak-peak intervals for (reliable) frequency domain measure computation, frequency output measures are still computed but treat them with caution! HF is usually computed over a minimum of 1 minute of good signal. LF is usually computed over a minimum of 2 minutes of good signal. VLF is usually computed over a minimum of 5 minutes of good signal. The LF/HF ratio is usually computed over minimum 24 hours, although an absolute minimum of 5 min has also been suggested. For more info see: Shaffer, F., Ginsberg, J.P. (2017). An Overview of Heart Rate Variability Metrics and Norms. Task Force of Pacing and Electrophysiology (1996), Heart Rate Variability in: European Heart Journal, vol.17, issue 3, pp354-381 This warning will not repeat' -------------- r7rfrqpsdZvlflfZhfzlf/hfi) zShort signal. z ---------Warning:--------- zQtoo few peak-peak intervals for (reliable) frequency domain measure computation, zKfrequency output measures are still computed but treat them with caution! zBHF is usually computed over a minimum of 1 minute of good signal. zBLF is usually computed over a minimum of 2 minutes of good signal.zCVLF is usually computed over a minimum of 5 minutes of good signal.zGThe LF/HF ratio is usually computed over minimum 24 hours, although an z4absolute minimum of 5 min has also been suggested. z8For more info see: Shaffer, F., Ginsberg, J.P. (2017), z:An Overview of Heart Rate Variability Metrics and Norms. zKTask Force of Pacing and Electrophysiology (1996), Heart Rate Variability, zTin: European Heart Journal, vol.17, issue 3, pp354-381 This warning will not repeatrkfftdrr)fsrr_npersegz?specified method incorrect, use 'fft', 'periodogram' or 'welch'gF%uk?g{Gz?)Zdxg333333?皙?Zp_totaldZvlf_percZlf_percZhf_percZlf_nuZhf_nuZinterp_rr_functionZinterp_rr_linspace)r#r!rOsumjoinwarningswarn UserWarningZcumsumintlinspacerrKr\fftfreqr&r(rrrEZtrapzr')rHZ welch_wsizeZsquare_spectrumrPr*Zdegree_smoothing_spliner+msgZrr_xZ resamp_factordatalenZrr_x_newZinterpolation_funcZ rr_interpdtr_Zfs_newrSYrTraZdfZ perc_factorZ nu_factorrrrrsd             &&&               Tg?rbcCstdt|t|}tdt|tj|tjd}t||dd}||} |rbtjj| |ddd} | dkrt| } tj j | d d } | t t | d } tj | | } | t t | d } tt| d } n| d kr.t| d krt| dt| d\} } n&t| dtjt| dd ddd\} } n*| dkrPt| dd d\} } ntd| t| |d<| |d<| |d<| |d<||fS)aestimates breathing rate Function that estimates breathing rate from heart rate signal. Upsamples the list of detected rr_intervals by interpolation then tries to extract breathing peaks in the signal. Parameters ---------- rr_list : 1d list or array list or array containing peak-peak intervals method : str method to use to get the spectrogram, must be 'fft' or 'welch' default : fft filter_breathing : bool whether to filter the breathing signal derived from the peak-peak intervals default : True bw_cutoff : list or tuple breathing frequency range expected default : [0.1, 0.4], meaning between 6 and 24 breaths per minute measures : dict dictionary object used by heartpy to store computed measures. Will be created if not passed to function. working_data : dict dictionary object that contains all heartpy's working data (temp) objects. will be created if not passed to function Returns ------- measures : dict dictionary object used by heartpy to store computed measures. Examples -------- Normally this function is called during the process pipeline of HeartPy. It can of course also be used separately. Let's load an example and get a list of peak-peak intervals >>> import heartpy as hp >>> data, _ = hp.load_exampledata(0) >>> wd, m = hp.process(data, 100.0) Breathing is then computed with the function >>> m, wd = calc_breathing(wd['RR_list_cor'], measures = m, working_data = wd) >>> round(m['breathingrate'], 3) 0.171 There we have it, .17Hz, or about one breathing cycle in 6.25 seconds. r)ZdtyperRrYrZbandpass)cutoffr)Z filtertyper\gMbP?r]rri0ur[r` N)Za_minZa_maxr)r_ZnfftzJBreathing rate extraction method not understood! Must be 'welch' or 'fft'!Z breathingrateZbreathing_signalZ breathing_psdZ breathing_frq)r!rjr#rdZint32rhp filteringZ filter_signalrCr\rkr&rir(r'rZcliprrEZargmax)ZrrlistrHZfilter_breathingZ bw_cutoffrPr*r>Zx_newinterpZ breathingrmrSrorTrrrrhs>;  c Cs2g}g}tt|ddD]N}|d||d|ddkr||d|||d|dqqt|}t|}||td}||td}tt|} tt|} tj| | } | |d<| |d<| |d<| | |d <i|d <||d d <||d d <||d d <||d d<|S)acomputes poincare parameters Function that takes peak-peak intervals and computes poincare parameters: [0] standard deviation perpendicular to identity line (SD1) [1] standard deviation along identity line (SD2) [2] area of ellipse described by SD1 and SD2 [3] SD1/SD2 ratio Based on: "Shaffer, F., Ginsberg, J.P. (2017), An Overview of Heart Rate Variability Metrics and Norms" Parameters ---------- rr_list : 1d array or list list or array containing peak-peak intervals rr_mask : 1d array or list list or array containing mask for rejected peak-peak intervals measures : dict dictionary object used by heartpy to store computed measures. Will be created if not passed to function. working_data : dict dictionary object that contains all heartpy's working data (temp) objects. will be created if not passed to function Returns ------- working_data : dict dictionary object that contains all heartpy's working data (temp) objects. measures : dict dictionary object used by heartpy to store computed measures. poincare values are appended to measures['poincare'] r6rrrrsd1sd2szsd1/sd2Zpoincarex_plusx_minusx_onex_two)r&r#rGr!rFrLvarpi) r+r:rPr*rxryrrzr{rurvrwrrr calc_poincares0(        r~)r<)__doc__rfnumpyr!Zscipy.interpolaterZ scipy.signalrrZ datautilsrrrr rsr Zheartpyrr__all__r r r rrrrr~rrrrs8   > E2 lQ] `