2023-09-17 00:46:14 +08:00
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#!/usr/bin/python
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# -*- coding: UTF-8 -*-
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"""
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@author:andrew
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@file:ResultSummary.py
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@email:admin@marques22.com
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@email:2021022362@m.scnu.edu.cn
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@time:2023/09/15
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"""
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import numpy as np
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import pandas as pd
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def AnalyseSegment(df_segment, thresh=0.5, thresh_event_interval=2, thresh_event_length=8,
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save_path=None, true_sleep_time=0):
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df_segment["thresh_Pred"] = df_segment["pred"].apply(lambda x: 1 if x > thresh else 0)
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thresh_Pred = df_segment["thresh_Pred"].values
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thresh_Pred2 = thresh_Pred.copy()
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# 扩充
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indices = np.where(thresh_Pred == 1)[0]
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for i in range(len(indices) - 1):
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if indices[i + 1] - indices[i] <= thresh_event_interval:
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thresh_Pred2[indices[i]:indices[i + 1]] = 1
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# 事件判断
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# 如果连续的1的长度大于阈值，则判断为事件，记录起止位置
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diffs = np.diff(thresh_Pred2, prepend=0, append=0)
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start_indices = np.where(diffs == 1)[0]
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end_indices = np.where(diffs == -1)[0]
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event_indices = np.where(end_indices - start_indices >= thresh_event_length)[0]
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result = [(start_indices[i], end_indices[i]) for i in event_indices]
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df_event = pd.DataFrame(result, columns=["start", "end"])
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df_event["length"] = df_event["end"] - df_event["start"]
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df_event["start"] = df_event["start"] - 29
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df_event["end"] = df_event["end"] - 29
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if save_path is not None:
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df_event.to_csv(save_path, index=False)
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# 根据AHI评估严重程度
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if true_sleep_time is 0:
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record_length = len(df_segment) / 60 / 60
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AHI = len(result) / record_length
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else:
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record_length = true_sleep_time / 60
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AHI = len(result) / record_length
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if AHI < 5:
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severity = "Healthy"
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elif AHI < 15:
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severity = "Mild"
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elif AHI < 30:
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severity = "Moderate"
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else:
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severity = "Severe"
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SA_HYP_count = len(result)
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print("patient: ", save_path.stem)
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print("Event number: ", SA_HYP_count)
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2023-09-17 10:04:35 +08:00
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print("Record length (hours): ", round(record_length, 2))
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2023-09-17 00:46:14 +08:00
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print("AHI: ", AHI)
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print("Severity: ", severity)
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print("=====================================")
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return SA_HYP_count, AHI, severity
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