バレーボール各国代表チームのレーティング手法の提案および結果予測・大会形式評価への応用

要旨ありスポーツ統計科学の新たな挑戦原著論

Detecting the snore related sound using neural network based technique

Snoring is the most common and characteristic symptom of the Obstructive Sleep Apnea(OSA). The snore sound has been recently recorded during sleep for the purpose of OSA screening. The snore-related sound (SRS) as well as the silence is included in the recorded sound. The SRS detection plays an important role as a first step in snore segmentation. However, the SRS is complex signal, and at both high and low signals to noize ratio (SNR). And thus, the complexity and low SNR of signals make it a challenging task to detect them in the recorded sound. In this paper, we propose the novel method to detect automatically SRS by using the noise-robust neural network technique. The performance of the proposed method is evaluated on the clinical SRS data and compared with that of conventional zero-crossing-based method. We show that the proposed method can detect accurately the SRS compared to the conventional method. Even at very low SNR, the proposed method works within the detection error of 0.12[s]

Discriminating apneic snorers and benign snorers based on snoring formant extracted via a noise-robust linear prediction technique

Snoring is the earliest and the most common symptom of Obstructive Sleep Apnea (OSA). Quantitative analysis of snoring, however, is not used at present in the clinical diagnosis of the disease. Several researchers have reported differences in the formant frequencies of Apneic and benign snoring sounds (SS) based on linear prediction coding (LPC) analysis. However, SS is complex signal and at local low signal to noise ratio (SNR). This signal complexity should reduce the accuracy of formant estimation. In this paper, we propose a novel approach to the diagnosis of OSA based on the formants of SSs, extracted via a noise-robust linear prediction technique. The proposed method and existing LPC-based method are compared via a measure, a which indicates the standard deviation of first formant frequencies. The performance of the proposed method was evaluated on a database of clinical snoring sounds recorded overnight in the laboratory of a hospital sleep diagnostic center. Compared with existing LPC-based method, we show that the proposed method can differentiate (sensitivity: 88.9%, specificity: 88.9%, AUC: 0.85) between benign snoring (Apnea Hypopnea Index, AHI = 6.0 ± 3.2 event/h; 6188 episodes) and apneic snoring (AHI = 40.7 ± 20.2 event/h; 14066 episodes)