# Signal guides

Choose a signal to see focused examples and saved plots. Start with the [quickstart](https://ambiqai.github.io/physiokit/tutorial/quickstart/) if you want a complete synthetic ECG workflow before exploring individual functions.

## Choose a signal

- [ECG](https://ambiqai.github.io/physiokit/reference/ecg/): Clean waveforms, find R-peaks, derive RR intervals, and generate synthetic ECG.
- [PPG](https://ambiqai.github.io/physiokit/reference/ppg/): Find pulse peaks and explore heart rate, respiratory rate, and SpO₂ examples.
- [Respiration](https://ambiqai.github.io/physiokit/reference/rsp/): Find respiratory peaks and calculate rate from sampled breathing signals.
- [Motion](https://ambiqai.github.io/physiokit/reference/imu/): Calculate ENMO, tilt angles, and activity counts from accelerometer data.
- [Heart rate variability](https://ambiqai.github.io/physiokit/reference/hrv/): Calculate time- and frequency-domain measures from beat intervals.
- [Signal utilities](https://ambiqai.github.io/physiokit/reference/signal/): Find shared filtering, resampling, smoothing, and transform examples.

## Short examples

### Compute heart rate from ECG peaks

```python
import physiokit as pk

fs = 250
ecg, segments, fiducials = pk.ecg.synthesize(
    signal_length=10 * fs,
    sample_rate=fs,
    heart_rate=72,
    leads=1,
)
ecg = pk.ecg.clean(ecg.squeeze(), sample_rate=fs)

bpm, qos = pk.ecg.compute_heart_rate(ecg, sample_rate=fs, method="peak")
print(bpm, qos)
```

### Find PPG peaks

```python
import numpy as np
import physiokit as pk

fs = 100
t = np.arange(0, 8, 1 / fs)
ppg = np.sin(2 * np.pi * 1.1 * t)

peaks = pk.ppg.find_peaks(ppg, sample_rate=fs)
print(len(peaks))
```

### Respiratory rate from RSP using FFT

```python
import numpy as np
import physiokit as pk

fs = 25
t = np.arange(0, 40, 1 / fs)
rsp = np.sin(2 * np.pi * 0.2 * t)

bpm, qos = pk.rsp.compute_respiratory_rate(rsp, sample_rate=fs, method="fft")
print(bpm, qos)
```

### IMU ENMO and tilt

```python
import numpy as np
import physiokit as pk

x = np.array([0, 0.1, 0.2])
y = np.array([0, 0.1, 0.2])
z = np.array([1, 1.0, 1.1])

enmo = pk.imu.compute_enmo(x, y, z)
angles = pk.imu.compute_tilt_angles(x, y, z, in_radians=False)
print(enmo, angles)
```

### Signal helpers: filter and FFT

```python
import numpy as np
import physiokit as pk

fs = 100
t = np.arange(0, 5, 1 / fs)
sig = np.sin(2 * np.pi * 2 * t) + 0.2 * np.random.randn(t.size)

clean = pk.signal.filter_signal(sig, lowcut=0.5, highcut=20, sample_rate=fs)
freqs, sp = pk.signal.compute_fft(clean, sample_rate=fs)
print(freqs[np.argmax(np.abs(sp))])
```