Key result
ECG-Derived Respiration (EDR) using linear discriminate classification identified normal or abnormal (apnea) epochs with an accuracy of >79.66%.
Why the study?
Can ECG-Derived Respiration accurately classify normal and abnormal breathing epochs in sleep-disordered breathing?
Can ECG-Derived Respiration accurately classify normal and abnormal breathing epochs in sleep-disordered breathing?
ECG-Derived Respiration combined with linear discriminant classification offers a noninvasive, low-cost method to detect sleep apnea with nearly 80% accuracy.
May enable low-cost ECG apnea screening; leaves open clinical adoption pending prospective validation.
Sleeping is an important part of human's life because of its state of unconsciousness and relaxation. According to today's common problems, such as increased risks of social, economic and healthcare, disturbances or interruptions in breathing pattern may occur during sleep or it may be discontinued that knows as Sleep Disorders Breath(SDB). Thus, the researchers used an abnormal breathing pattern and characteristics of sleep to detect disorder and help these patients. In this way without any extra equipment, using ECG-Derived Respiration from Electrocardiogram can be a noninvasive, low-cost measurement of respiration wave. In this research, three major methods reinvestigate and the accurate method with the accuracy of 0.9 selected. EDR can be classified by a linear discriminate classification to normal or abnormal (apnea) epoch. In conclusion part, three methods were compared and by selecting the more accurate one, EDR epoch were classified by more than 79.66% accuracy.
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Tork et al. (2013) studied Sleep Disordered Breathing (SDB). ECG-Derived Respiration (EDR) was evaluated on Classification accuracy of normal vs abnormal (apnea) epochs. ECG-Derived Respiration (EDR) using linear discriminate classification identified normal or abnormal (apnea) epochs with an accuracy of >79.66%.
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