0945 A Robust Hybrid algorithm for automatic respiratory events scoring in adults

Abstract

Abstract Introduction Even though Polysomnography has many advantages, manual scoring is a labor intensive and time consuming. Therefore, automatic scoring system using a deep learning algorithm has been developed and commercialized for clinical sites. However, it is challenging to explain the results of respiratory events and to measure a precise event’s duration. To overcome these challenges, a new hybrid algorithm was proposed to explain why this event was categorized and provided the accurate duration of respiratory events. Methods In order to classify respiratory events - OSA, MSA, CSA, O-hypopnea, and C-hypopnea, the proposed algorithm consists of two algorithms – deep learning and post processing algorithms. Deep learning algorithm was used to detect candidates of respiratory events and post processing algorithm was used to determine the final classification and to measure duration of events. Deep learning model consisted of five layers: a perceptron layer, three consecutive layers of long short-term memory (LSTM) cells, and two more perceptron layers. A total of 1,000 case PSG data were enrolled to develop this algorithm. To classify respiratory events from the results provided by the deep learning algorithm, auto-correlation, peak detection, de-noise filtering and transition detection were used to obtain respiratory rate and measure signal amplitude. Using these algorithm’s results, amplitude and time of baseline breathing and respiratory events were calculated and classified based on the AASM’s guidelines. Results To evaluate the performance of proposed algorithm, 30 PSG data were selected and compared with the results of deep learning algorithm. The test dataset consisted of thirty subjects with 15 OSA, 5 PLMS, 2 insomnia and 8 healthy. The kappa value of deep learning algorithms showed 0.77 (0.759, 0.78) and that of proposed algorithm was 0.838 (0.832, 0.843). They showed better agreement by 0.068 than that of deep learning algorithm. In addition, the proposed algorithm provided the classification results such as OSA, MSA, CSA, O-hypopnea and C-hypopnea and explanations of the reason of event detection. Conclusion The proposed algorithm showed better agreement and versatile classified results. This algorithm will be adopted for new automatic sleep scoring solution and this solution will be very helpful to use in real clinical sites. Support (if any)