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  • 學位論文

以心率變異度辨識睡眠呼吸中止症之軟體分析系統研發

Development of Software Analysis System Using Heart Rate Variability to Identify Sleep Apnea

指導教授 : 婁世亮 葛宗融

摘要


睡眠呼吸中止症發生時會導致心跳加快、血氧濃度下降以及呼吸暫停等症狀,造成睡眠中斷,進而影響睡眠品質。一般診斷睡眠呼吸中止症的方式較繁瑣,故本研究目的為開發一套以心率變異度辨識睡眠呼吸中止症軟體分析系統,初步判斷是否患有睡眠呼吸中止症。使用網路上現有的UCD Sleep Apnea Database ,藉此開發本分析軟體的功能,其功能包含計算心跳速率、重新取樣、快速傅立葉轉換、計算頻譜功率以及開發辨識睡眠呼吸中止症之演算法。本研究開發的軟體與Kubios分析軟體所產生的心率變異度結果具有相當高的相關性,相關係數皆大於0.96,證明本研究開發的軟體功能是可靠的。在演算法的部分,使用線性回歸的方式進行偵測,線性回歸結果與真實發生次數的相關係數為0.4727,此結果的相關係數並不高,準確性結果只有55%。為了改善此結果,進行類神經網路的訓練,類神經網路辨識睡眠呼吸事件結果的準確性為71%。綜合以上論述,本研究已初步開發出使用心率變異度辨識睡眠呼吸中止症之分析軟體,未來將持續改善本分析軟體的演算法,提高辨識睡眠呼吸中止症之準確性及可信度。

並列摘要


When sleep apnea occurs, it will increase heart rate, decrease pulse oximetry (SPO2) and cause sleep disturbances thereby affecting the quality of sleep. Diagnosis of sleep apnea is generally complicated. The purpose of this study is to develop an analysis software system based on heart rate variability to identify sleep apnea and to facilitate an early diagnosis of the symptom. We use the UCD Sleep Apnea Database to develop algorithms of identifying sleep apnea by analysis software. The function includes heart rate calculation, resampling, Fast Fourier Transform and power spectral density. The analysis software in this study has very high correlation with the Kubios analysis software (correlation coefficients >0.96). It shows highly reliability of analysis software. In part of algorithm, linear regression approach is used to determine sleep apnea syndrome. The linear regression correlation coefficient which is 0.4727 and the accuracy is 55%. In order to improve the accuracy, neural network training was carried out and it increased the accuracy of sleep apnea events to 71%. In conclusion, the present study developed an analysis software system based on heart rate variability to identify sleep apnea. The improvement of the analysis software algorithms will be continued to level up the accuracy and reliability of sleep apnea recognition.

參考文獻


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