Run PPG benchmark directly on the Capnobase download (no need to unpa…

…ck data first).

biopeaks/benchmarks/benchmark_PPG_local.py

@@ -0,0 +1,44 @@
+# -*- coding: utf-8 -*-
+
+import h5py
+import numpy as np
+from pathlib import Path
+from biopeaks.heart import ppg_peaks
+from wfdb.processing import compare_annotations
+
+
+data_dir = Path(".../TBME2013-PPGRR-Benchmark_R3/data")    # replace with your local "data" directory once you've downloaded the database
+
+sfreq = 300
+tolerance = int(np.rint(.05 * sfreq))    # in samples; 50 milliseconds in accordance with Elgendi et al., 2013, doi:10.1371/journal.pone.0076585
+print(f"Setting tolerance for match between algorithmic and manual annotation"
+      f" to {tolerance} samples, corresponding to 50 milliseconds at a sampling rate of {sfreq}.")
+
+sensitivity = []
+precision = []
+
+for subject in data_dir.iterdir():
+
+    f = h5py.File(subject, "r")
+    record = np.ravel(f["signal"]["pleth"]["y"])
+    annotation = np.ravel(f["labels"]["pleth"]["peak"]["x"])
+
+    peaks = ppg_peaks(record, sfreq)
+
+    comparitor = compare_annotations(peaks, annotation, tolerance)
+    tp = comparitor.tp
+    fp = comparitor.fp
+    fn = comparitor.fn
+
+    sensitivity.append(float(tp) / (tp + fn))
+    precision.append(float(tp) / (tp + fp))
+
+    print(f"\nResults {subject}")
+    print("-" * len(str(subject)))
+    print(f"sensitivity = {sensitivity[-1]}")
+    print(f"precision = {precision[-1]}")
+
+print(f"\nAverage results over {len(precision)} records")
+print("-" * 31)
+print(f"sensitivity: mean = {np.mean(sensitivity)}, std = {np.std(sensitivity)}")
+print(f"precision: mean = {np.mean(precision)}, std = {np.std(precision)}")

docs/tests.md

@@ -17,15 +17,26 @@ pytest -v
 
 
 ## Extrema detection benchmarks
+
+### ECG
 To validate the performance of the ECG peak detector `heart.ecg_peaks()`, please install the [wfdb](https://github.com/MIT-LCP/wfdb-python) and [aiohttp](https://github.com/aio-libs/aiohttp):
 ```
 conda install -c conda-forge wfdb
 conda install -c conda-forge aiohttp
 ```
 
 You can then run the `benchmark_ECG_stream` script in the `benchmarks` folder. The script streams ECG and annotation files from the [Glasgow University Database (GUDB)](http://researchdata.gla.ac.uk/716/).
-You can select an experiment, ECG channel, and annotation file.
+You can select an experiment, ECG channel, and annotation file (for details have a look at the docstrings of `BenchmarkDetectorGUDB.benchmark_records()` in `benchmarks\benchmark_utils`).
+
+Alternatively, you can download the GUDB and run the `benchmark_ECG_local` script in the `benchmarks` folder. In the script, replace the `data_dir` with your local directory (see comments in the script).
+
+### PPG
 
 To validate the performance of the PPG peak detector `heart.ppg_peaks()`
-please download the [Capnobase IEEE TBME benchmark dataset](http://www.capnobase.org/index.php?id=857).
-After extracting the PPG signals and peak annotations you can run the `benchmark_PPG` script in the `benchmarks` folder.
+please download the [Capnobase IEEE TBME benchmark dataset](http://www.capnobase.org/index.php?id=857) and install [wfdb](https://github.com/MIT-LCP/wfdb-python) and [h5py](https://www.h5py.org/):
+```
+conda install -c conda-forge wfdb
+conda install -c conda-forge h5py
+```
+
+You can then run the `benchmark_PPG_local` script in the `benchmarks` folder. In the script, replace the `data_dir` with your local directory (see comments in the script).