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`find_local_peaks` ignores the sample at `i + radius`, so points on a rising slope are returned as peaks

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評估

難度
2/5
預估耗時
1-3 小時
新手友好度
84/100
Issue 類型
缺陷
描述清晰度
描述清楚
活躍度
活躍
技術堆疊
numpy, python
領域
data

研究方向

從 wfdb/processing/peaks.py 中的 find_local_peaks 開始，檢查其迴圈中的視窗切片。使用提供的重現來驗證每個半徑都會考慮右側邊界，然後檢查 qrs.py 中 XQRS 呼叫點的相容性。在不更改獨立的 flat-top TODO 的情況下，輸出與參考案例相符即表示完成。

由索引模型根據 Issue 內容生成。

描述

The docstring of wfdb.processing.find_local_peaks says a sample is a local peak "if it is the largest value within the samples on its left and right". The window it actually checks leaves out the right side:

# wfdb/processing/peaks.py
while i < radius + 1:
    if sig[i] == max(sig[: i + radius]):          # right side stops at i + radius - 1
...
while i < len(sig):
    if sig[i] == max(sig[i - radius : i + radius]):  # sample i + radius is never looked at

Python slices exclude the end, so sig[i + radius] is never compared. With radius=1 the right neighbour is not checked at all, so every sample on a rising edge counts as a peak. With bigger radii a sample still wins if the only larger value is exactly radius samples to its right.

Reproduction

import numpy as np
from wfdb.processing import find_local_peaks

def reference(sig, radius):
    # what the docstring describes
    out = []
    for i in range(len(sig)):
        lo, hi = max(0, i - radius), min(len(sig), i + radius + 1)
        if sig[i] == sig[lo:hi].max():
            out.append(i)
    return out

for sig, r in [
    (np.array([0, 1, 2, 3, 2, 1, 0], dtype=float), 1),
    (np.array([0, 1, 3, 2, 10, 5, 1, 0], dtype=float), 2),
]:
    print(sig.astype(int).tolist(), "radius", r)
    print("  find_local_peaks:", find_local_peaks(sig, r).tolist())
    print("  expected        :", reference(sig, r))

Output:

[0, 1, 2, 3, 2, 1, 0] radius 1
  find_local_peaks: [0, 1, 2, 3]
  expected        : [3]
[0, 1, 3, 2, 10, 5, 1, 0] radius 2
  find_local_peaks: [2, 4]
  expected        : [4]

In the first case indices 0, 1 and 2 are all on the way up to the real maximum at index 3. In the second case index 2 (value 3) is reported even though index 4 (value 10) is only 2 samples away.

Why it matters

XQRS uses this function to pick QRS candidates (qrs.py, lines 236 and 283), so a candidate can end up on the slope next to the real peak, not on the peak itself. With real ECG sampling rates the radius is large, so this probably shows up rarely there, but anyone calling find_local_peaks directly with a small radius gets wrong results.

Possible fix

Include the right edge in both slices (sig[: i + radius + 1] and sig[i - radius : i + radius + 1]). The third loop is unreachable (already reported in #474), so it can go away in the same change. I have not touched the flat-top case that the TODO comment mentions; that looks like a separate question.

Versions

  • wfdb 4.3.1 from PyPI, and also current main (f627b5f), same output
  • numpy 2.5.3, Python 3.12.13, macOS 26.4.1
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從這裡開始

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