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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def ecg_process(ecg, rsp=None, sampling_rate=1000, filter_type="FIR", filter_band="bandpass", filter_frequency=[3, 45], segmenter="hamilton", quality_model="default", hrv_features=["time", "frequency"], age=None, sex=None, position=None):
 """ Automated processing of ECG and RSP signals. Parameters ecg : list or ndarray ECG signal array. rsp : list or ndarray Respiratory (RSP) signal array. sampling_rate : int Sampling rate (samples/second). filter_type : str Can be Finite Impulse Response filter ("FIR"), Butterworth filter ("butter"), Chebyshev filters ("cheby1" and "cheby2"), Elliptic filter ("ellip") or Bessel filter ("bessel"). filter_band : str Band type, can be Low-pass filter ("lowpass"), High-pass filter ("highpass"), Band-pass filter ("bandpass"), Band-stop filter ("bandstop"). filter_frequency : int or list Cutoff frequencies, format depends on type of band: "lowpass" or "bandpass": single frequency (int), "bandpass" or "bandstop": pair of frequencies (list). segmenter : str The cardiac phase segmenter. Can be "hamilton", "gamboa", "engzee", "christov" or "ssf". See :func:`neurokit.ecg_preprocess()` for details. quality_model : str Path to model used to check signal quality. "default" uses the builtin model. None to skip this function. hrv_features : list What HRV indices to compute. Any or all of 'time', 'frequency' or 'nonlinear'. None to skip this function. age : float Subject's age for adjusted HRV. sex : str Subject's gender ("m" or "f") for adjusted HRV. position : str Recording position. To compare with data from Voss et al. (2015), use "supine". Returns processed_ecg : dict Dict containing processed ECG features. Contains the ECG raw signal, the filtered signal, the R peaks indexes, HRV features, all the heartbeats, the Heart Rate, the RSP filtered signal (if respiration provided) and the respiratory sinus arrhythmia (RSA). Example Notes *Details* - **Cardiac Cycle**: A typical ECG showing a heartbeat consists of a P wave, a QRS complex and a T wave.The P wave represents the wave of depolarization that spreads from the SA-node throughout the atria. The QRS complex reflects the rapid depolarization of the right and left ventricles. Since the ventricles are the largest part of the heart, in terms of mass, the QRS complex usually has a much larger amplitude than the P-wave. The T wave represents the ventricular repolarization of the ventricles. On rare occasions, a U wave can be seen following the T wave. The U wave is believed to be related to the last remnants of ventricular repolarization. - **RSA**: Respiratory sinus arrhythmia (RSA) is a naturally occurring variation in heart rate that occurs during the breathing cycle, serving as a measure of parasympathetic nervous system activity. See :func:`neurokit.ecg_rsa()` for details. - **HRV**: Heart-Rate Variability (HRV) is a finely tuned measure of heart-brain communication, as well as a strong predictor of morbidity and death (Zohar et al., 2013). It describes the complex variation of beat-to-beat intervals mainly controlled by the autonomic nervous system (ANS) through the interplay of sympathetic and parasympathetic neural activity at the sinus node. In healthy subjects, the dynamic cardiovascular control system is characterized by its ability to adapt to physiologic perturbations and changing conditions maintaining the cardiovascular homeostasis (Voss, 2015). In general, the HRV is influenced by many several factors like chemical, hormonal and neural modulations, circadian changes, exercise, emotions, posture and preload. There are several procedures to perform HRV analysis, usually classified into three categories: time domain methods, frequency domain methods and non-linear methods. See :func:`neurokit.ecg_hrv()` for a description of indices. - **Adjusted HRV**: The raw HRV features are normalized :math:`(raw - Mcluster) / sd` according to the participant's age and gender. In data from Voss et al. (2015), HRV analysis was performed on 5-min ECG recordings (lead II and lead V2 simultaneously, 500 Hz sample rate) obtained in supine position after a 5–10 minutes resting phase. The cohort of healthy subjects consisted of 782 women and 1124 men between the ages of 25 and 74 years, clustered into 4 groups: YF (Female, Age = [25-49], n=571), YM (Male, Age = [25-49], n=744), EF (Female, Age = [50-74], n=211) and EM (Male, Age = [50-74], n=571). - **Systole/Diastole**: One prominent channel of body and brain communication is that conveyed by baroreceptors, pressure and stretch-sensitive receptors within the heart and surrounding arteries. Within each cardiac cycle, bursts of baroreceptor afferent activity encoding the strength and timing of each heartbeat are carried via the vagus and glossopharyngeal nerve afferents to the nucleus of the solitary tract. This is the principal route that communicates to the brain the dynamic state of the heart, enabling the representation of cardiovascular arousal within viscerosensory brain regions, and influence ascending neuromodulator systems implicated in emotional and motivational behaviour. Because arterial baroreceptors are activated by the arterial pulse pressure wave, their phasic discharge is maximal during and immediately after the cardiac systole, that is, when the blood is ejected from the heart, and minimal during cardiac diastole, that is, between heartbeats (Azevedo, 2017). - **ECG Signal Quality**: Using the PTB-Diagnostic dataset available from PhysioNet, we extracted all the ECG signals from the healthy participants, that contained 15 recording leads/subject. We extracted all cardiac cycles, for each lead, and downsampled them from 600 to 200 datapoints. Note that we dropped the 8 first values that were NaNs. Then, we fitted a neural network model on 2/3 of the dataset (that contains 134392 cardiac cycles) to predict the lead. Model evaluation was done on the remaining 1/3. The model show good performances in predicting the correct recording lead (accuracy=0.91, precision=0.91). In this function, this model is fitted on each cardiac cycle of the provided ECG signal. It returns the probable recording lead (the most common predicted lead), the signal quality of each cardiac cycle (the probability of belonging to the probable recording lead) and the overall signal quality (the mean of signal quality). See creation `scripts <https://github.com/neuropsychology/NeuroKit.py/tree/master/utils/ecg_signal_quality_model_creation>`_. *Authors* - `Dominique Makowski <https://dominiquemakowski.github.io/>`_ - Rhenan Bartels (https://github.com/rhenanbartels) *Dependencies* - biosppy - numpy - pandas *See Also* - BioSPPY: https://github.com/PIA-Group/BioSPPy - hrv: https://github.com/rhenanbartels/hrv - RHRV: http://rhrv.r-forge.r-project.org/ References - Heart rate variability. (1996). Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J, 17, 354-381. - Voss, A., Schroeder, R., Heitmann, A., Peters, A., & Perz, S. (2015). Short-term heart rate variability—influence of gender and age in healthy subjects. PloS one, 10(3), e0118308. - Zohar, A. H., Cloninger, C. R., & McCraty, R. (2013). Personality and heart rate variability: exploring pathways from personality to cardiac coherence and health. Open Journal of Social Sciences, 1(06), 32. - Smith, A. L., Owen, H., & Reynolds, K. J. (2013). Heart rate variability indices for very short-term (30 beat) analysis. Part 2: validation. Journal of clinical monitoring and computing, 27(5), 577-585. - Azevedo, R. T., Garfinkel, S. N., Critchley, H. D., & Tsakiris, M. (2017). Cardiac afferent activity modulates the expression of racial stereotypes. Nature communications, 8. - Edwards, L., Ring, C., McIntyre, D., & Carroll, D. (2001). Modulation of the human nociceptive flexion reflex across the cardiac cycle. Psychophysiology, 38(4), 712-718. - Gray, M. A., Rylander, K., Harrison, N. A., Wallin, B. G., & Critchley, H. D. (2009). Following one's heart: cardiac rhythms gate central initiation of sympathetic reflexes. Journal of Neuroscience, 29(6), 1817-1825. """

    # Preprocessing
    # =============
    processed_ecg = ecg_preprocess(ecg,
                                   sampling_rate=sampling_rate,
                                   filter_type=filter_type,
                                   filter_band=filter_band,
                                   filter_frequency=filter_frequency,
                                   segmenter=segmenter)

    # Signal quality
    # ===============
    if quality_model is not None:
        quality = ecg_signal_quality(cardiac_cycles=processed_ecg["ECG"]["Cardiac_Cycles"], sampling_rate=sampling_rate, rpeaks=processed_ecg["ECG"]["R_Peaks"], quality_model=quality_model)
        processed_ecg["ECG"].update(quality)
        processed_ecg["df"] = pd.concat([processed_ecg["df"], quality["ECG_Signal_Quality"]], axis=1)

    # HRV
    # =============
    if hrv_features is not None:
        hrv = ecg_hrv(rpeaks=processed_ecg["ECG"]["R_Peaks"], sampling_rate=sampling_rate, hrv_features=hrv_features)
        try:
            processed_ecg["df"] = pd.concat([processed_ecg["df"], hrv.pop("df")], axis=1)
        except KeyError:
            pass
        processed_ecg["ECG"]["HRV"] = hrv
        if age is not None and sex is not None and position is not None:
            processed_ecg["ECG"]["HRV_Adjusted"] = ecg_hrv_assessment(hrv, age, sex, position)

    # RSP
    # =============
    if rsp is not None:
        rsp = rsp_process(rsp=rsp, sampling_rate=sampling_rate)
        processed_ecg["RSP"] = rsp["RSP"]
        processed_ecg["df"] = pd.concat([processed_ecg["df"], rsp["df"]], axis=1)

        # RSA
        # =============
        rsa = ecg_rsa(processed_ecg["ECG"]["R_Peaks"], rsp["df"]["RSP_Filtered"], sampling_rate=sampling_rate)
        processed_ecg["ECG"]["RSA"] = rsa
        processed_ecg["df"] = pd.concat([processed_ecg["df"], rsa.pop("df")], axis=1)

    return(processed_ecg)