Assessment of Dual-Tree Complex Wavelet Transform to improve SNR in collaboration with Neuro-Fuzzy System for Heart Sound Identification

none6siThe research paper proposes a novel denoising method to improve the outcome of heartsound (HS)-based heart-condition identification by applying the dual-tree complex wavelet transform (DTCWT) together with the adaptive neuro-fuzzy inference System (ANFIS) classifier. The method consists of three steps: first, preprocessing to eliminate 50 Hz noise; second, applying four successive levels of DTCWT to denoise and reconstruct the time-domain HS signal; third, to evaluate ANFIS on a total of 2735 HS recordings from an international dataset (PhysioNet Challenge 2016). The results show that the signal-to-noise ratio (SNR) with DTCWT was significantly improved (p < 0.001) as compared to original HS recordings. Quantitatively, there was an 11% to many decibel (dB)-fold increase in SNR after DTCWT, representing a significant improvement in denoising HS. In addition, the ANFIS, using six time-domain features, resulted in 55–86% precision, 51–98% recall, 53–86% f-score, and 54–86% MAcc compared to other attempts on the same dataset. Therefore, DTCWT is a successful technique in removing noise from biosignals such as HS recordings. The adaptive property of ANFIS exhibited capability in classifying HS recordings.Special Issue “Biomedical Signal Processing”, Section BioelectronicsopenBassam Al-Naami, Hossam Fraihat, Jamal Al-Nabulsi, Nasr Y. Gharaibeh, Paolo Visconti, Abdel-Razzak Al-HinnawiAl-Naami, Bassam; Fraihat, Hossam; Al-Nabulsi, Jamal; Gharaibeh, Nasr Y.; Visconti, Paolo; Al-Hinnawi, Abdel-Razza

Redes Neuronais Pré-Treinadas na Classificação Automática de Sons Cardíacos

As doenças cardiovasculares são uma das principais causas de morte e hospitalização, tanto em países desenvolvidos como em desenvolvimento. O seu diagnóstico requer intervenção profissional e equipamento específico, sendo normalmente dispendioso. O desenvolvimento de algoritmos capazes de segmentar e classificar sinais dos batimentos cardíacos beneficia esta área, uma vez que muitas doenças cardiovasculares se manifestam como irregularidades nos mesmos. Estes algoritmos servirão de apoio ao diagnóstico para os profissionais de saúde e oferecem a possibilidade de serem incorporados em dispositivos próprios para uso doméstico reduzindo a necessidade de consumo de recursos hospitalares ou de centros privados de saúde. No entanto, até ao momento, não existem implementações, clínicas ou não, destes métodos. Nos últimos anos, vários algoritmos de classificação baseados em diferentes técnicas surgiram e bases de dados vastas e de livre acesso foram disponibilizadas procurando estabelecer um ponto de comparação da eficácia dos mesmos. A presente dissertação explora a eficácia da utilização de redes neuronais pré-treinadas na classificação dos sinais disponibilizados no PhysioNet/CinC Challenge 2016, uma das maiores bases de dados de fonocardiogramas já reunida. A melhor rede gerada obteve uma precisão de classificação de 80.85%, uma sensibilidade de 79.77% e uma especificidade de 81.94%, estando em linha com resultados obtidos por métodos diferentes e recorrendo a menos pré-processamento do sinal.Cardiovascular diseases are the leading cause of hospitalization and death, in both developed and developing countries. Its diagnosis requires expert intervention as well as specialized equipment, being costly. The development of algorithms capable of segmenting and classifying signals from the heartbeat benefits this field since many cardiovascular diseases manifest themselves through irregular heartbeats. These algorithms will serve as a clinical decision support system for health professionals and offer the opportunity of creating domestic devices, reducing the need for hospital and private centres resource consumption. However, at the moment, there is no clinical or otherwise implementation of such technology. In the last years, many classification algorithms working on different techniques have emerged and vast open source databases have been made available looking to establish a comparison between those methods. This dissertation aims to test the efficiency of pre-trained neural networks in the classification of signals retrieved from the PhysioNet/CinC Challenge 2016, one of the largest collection of PCG ever assembled. Our best network achieved an accuracy of 80.85%, a recall of 79.77% and a specificity of 81.94%, being competitive with other methods and requiring less signal processing