Respiratory (RSP)
Respiratory rate is often measured on the chest using a respiration belt or a respiratory inductance plethysmography (RIP) sensor. PhysioKit provides a set of functions to process RSP signals. The functions can be used to generate synthetic RSP signals, clean noisy RSP signals, extract respiratory peaks, compute respiratory rate, and compute dual band metrics.
Synthetic RSP
We can generate a synthetic RSP signal using the rsp.synthesize function. The function returns a numpy array with the RSP signal. The duration parameter specifies the length of the signal in seconds. The sample_rate parameter specifies the sampling rate in Hz. The respiratory_rate parameter specifies the respiratory rate in breaths per minute (BPM). The function returns a numpy array with the RSP signal.
Example
In the following snippet, we generate a synthetic RSP inductance band signal with a respiratory rate of 12 BPM sampled at 1000 Hz.
import physiokit as pk
sample_rate = 1000 # Hz
respiratory_rate = 12 # BPM
rsp, segs, fids = pk.rsp.synthesize(
signal_length=signal_length,
sample_rate=sample_rate,
respiratory_rate=respiratory_rate,
)
Noise Injection
We can additionally add noise to generate a more realistic RSP signal.
Example
Below adds baseline wander, powerline noise, and custom noise sources to the synthetic RSP signal.
# Add baseline wander
rsp_noise = pk.signal.add_baseline_wander(
data=rsp,
amplitude=2,
frequency=.05,
sample_rate=sample_rate
)
# Add powerline noise
rsp_noise = pk.signal.add_powerline_noise(
data=rsp_noise,
amplitude=0.05,
frequency=60,
sample_rate=sample_rate
)
# Add additional noise sources
rsp_noise = pk.signal.add_noise_sources(
data=rsp_noise,
amplitudes=[0.05, 0.05],
frequencies=[10, 20],
noise_shapes=["laplace", "laplace"],
sample_rate=sample_rate
)
Sanitize RSP
We can clean the RSP signal using the rsp.clean function. By default, the routine implements a bandpass filter with cutoff frequencies of 0.05 Hz and 3 Hz. The lowcut and highcut parameters can be used to specify the cutoff frequencies. The order parameter specifies the order of the filter. The sample_rate parameter specifies the sampling rate in Hz. The function returns a numpy array with the cleaned RSP signal.
Example
In the following snippet, we clean the noisy RSP signal using a bandpass filter with cutoff frequencies of 0.05 Hz and 3 Hz.
...
# Clean RSP signal
rsp_clean = pk.rsp.clean(
data=rsp_noise,
lowcut=0.05,
highcut=3,
order=3,
sample_rate=sample_rate
)
Extract Respiratory Peaks
A common task in RSP processing is to extract respiratory peaks. The rsp.find_peaks function implements a simple peak detection algorithm. The function returns a numpy array with the indices of the peaks.
Example
In the following snippet, we extract respiratory peaks from the cleaned RSP signal.
...
# Extract respiratory cycles
peaks = pk.rsp.find_peaks(data=rsp_clean, sample_rate=sample_rate)
# Compute RR-intervals
rri = pk.rsp.compute_rr_intervals(peaks=peaks)
# Filter RR-intervals
mask = pk.rsp.filter_rr_intervals(rr_ints=rri, sample_rate=sample_rate)
# Keep normal RR-intervals
peaks_clean = peaks[mask == 0]
rri_clean = rri[mask == 0]
Compute Respiratory Rate
The rsp.compute_respiratory_rate function computes the respiratory rate in breaths per minute (BPM) based on the selected method. The peak method computes respiratory rate based on identified respiratory peaks whereas fft method uses FFT to compute the respiratory rate. The function returns the respiratory rate in breaths per minute (BPM) along with a "quality of signal" (QoS) metric. The QoS metric is based on the selected method.
Example
In the following snippet, we compute the respiratory rate based on the identified respiratory peaks.
# Compute respiratory rate
rr_bpm, rr_qos = pk.rsp.compute_respiratory_rate(
data=rsp_clean,
method="fft",
sample_rate=sample_rate,
lowcut=0.05,
highcut=1
)
Respiratory Rate: 12 BPM
Compute Dual Band Metrics
Using dual RIP bands, a ribcage (RC) band and a abdominal (AB) band, we can compute additional respiratory metrics. The rsp.compute_dual_band_metrics function computes the following metrics:
| Metric | Description |
|---|---|
| rc_rr | RC respiratory rate (BPM) |
| ab_rr | AB respiratory rate (BPM) |
| vt_rr | VT respiratory rate (BPM) |
| phase | Phase angle (degrees) |
| lbi | Labored breathing index |
| rc_lead | RC leads AB |
| rc_percent | Percent RC contribution |
| qos | Quality of signal (0-1) |
| rc_pk_freq | RC peak frequency (Hz) |
| rc_pk_pwr | RC peak power |
| ab_pk_freq | AB peak frequency (Hz) |
| ab_pk_pwr | AB peak power |
| vt_pk_freq | VT peak frequency (Hz) |
| vt_pk_pwr | VT peak power |
Example
In the following example, we generate synthetic RC and AB band data, compute the dual band metrics, and plot the results. We compute the metrics over a sliding window with a length of 10 seconds and an overlap of 1 second.
sample_rate = 1000
respiratory_rate = 12.2
rc_amp = 1.5
ab_amp = 1.0
dur_sec = 60
win_len = 10*sample_rate
ovl_len = 1*sample_rate
# Synthesize RC and AB band data
rc = rc_amp*pk.rsp.synthesize(
duration=dur_sec,
sample_rate=sample_rate,
respiratory_rate=respiratory_rate
)
ab = ab_amp*pk.rsp.synthesize(
duration=dur_sec,
sample_rate=sample_rate,
respiratory_rate=respiratory_rate
)
# Compute metrics over sliding window
ts_metrics, dual_metrics = [], []
for i in range(0, rc.size - win_len, ovl_len):
rc_win = rc[i:i+win_len]
ab_win = ab[i:i+win_len]
ts_metrics.append((i+win_len/2)/sample_rate)
dual_metrics.append(pk.rsp.compute_dual_band_metrics(
rc=rc_win,
ab=ab_win,
sample_rate=sample_rate,
pwr_threshold=0.9
))