def test_ppg_findpeaks():
sampling_rate = 500
ppg = nk.ppg_simulate(
duration=30,
sampling_rate=sampling_rate,
heart_rate=60,
frequency_modulation=0.01,
ibi_randomness=0.1,
drift=1,
motion_amplitude=0.5,
powerline_amplitude=0.1,
burst_amplitude=1,
burst_number=5,
random_state=42,
show=True,
)
ppg_cleaned_elgendi = nk.ppg_clean(ppg,
sampling_rate=sampling_rate,
method="elgendi")
info_elgendi = nk.ppg_findpeaks(ppg_cleaned_elgendi,
sampling_rate=sampling_rate,
show=True)
peaks = info_elgendi["PPG_Peaks"]
assert peaks.size == 29
assert peaks.sum() == 219763
def find_feature_points(signal, sampling_rate=50, threshold=0.5):
"""
寻找峰值以及槽点
:param signal: 初始数据 已经去噪
:param sampling_rate: 采样率
:param threshold: 过近的点可以认为是异常值 给予一个threshold去除异常识别的峰值点
:return:
"""
origin_signal = signal.copy()
peaks = nk.ppg_findpeaks(signal, sampling_rate=sampling_rate)
# 先找到峰值点
locmax, props = spsg.find_peaks(signal)
notches = []
other_notches = []
# 两种方法找到的共同峰值点
all_peaks = np.intersect1d(locmax, peaks["PPG_Peaks"])
mean_T = np.mean(np.diff(all_peaks))
# 真正的峰值点
true_peaks = [all_peaks[0]]
# 周期
T = []
for i in range(len(all_peaks) - 1):
if all_peaks[i + 1] - all_peaks[i] < threshold * mean_T:
continue
true_peaks.append(all_peaks[i + 1])
for i in range(len(true_peaks) - 1):
notches.append(true_peaks[i] +
np.argmin(origin_signal[true_peaks[i]:true_peaks[i +
1]]))
T.append(true_peaks[i + 1] - true_peaks[i])
for i in range(len(locmax) - 1):
other_notches.append(locmax[i] +
np.argmin(origin_signal[locmax[i]:locmax[i + 1]]))
other_peaks = list(set(locmax) - set(true_peaks))
other_notches = list(set(other_notches) - set(notches))
other_peaks = [i for i in other_peaks if origin_signal[i] > 0]
other_notches = [i for i in other_notches if origin_signal[i] > 0]
return {
"Peaks": true_peaks,
"Other Peaks": other_peaks,
"Other Notches": other_notches,
"Notches": notches,
"T": T,
"Cleaned Pulses": origin_signal
}
def neurokit_index(request):
data = pd.read_csv(
"/Users/siyuqian/Study/django-docker/712AF22B_Mar11_14-07-59.csv")
# Generate 15 seconds of PPG signal (recorded at 250 samples / second)
# ppg = nk.ppg_simulate(duration=15, sampling_rate=250, heart_rate=70)
ppg = nk.ppg_process(data['PPG'], sampling_rate=50)
# Clear the noise
ppg_clean = nk.ppg_clean(ppg)
# Peaks
peaks = nk.ppg_findpeaks(ppg_clean, sampling_rate=100)
# Compute HRV indices
hrv_indices = nk.hrv(peaks, sampling_rate=100, show=True)
result = hrv_indices.to_json()
parsed = json.loads(result)
context = {'response': json.dumps(parsed)}
return render(request, 'neurokit/neurokit_index.html', context)