Classification of phonocardiograms with convolutional neural networks

The diagnosis of heart diseases from heart sounds is a matter of many years. This is the effect of having too many people with heart diseases in the world. Studies on heart sounds are usually based on classification for helping doctors. In other words, these studies are a substructure of clinical decision support systems. In this study, three different heart sound data in the PASCAL Btraining data set such as normal, murmur, and extrasystole are classified. Phonocardiograms which were obtained from heart sounds in the data set were used for classification. Both Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) were used for classification to compare obtained results. In these studies, the obtained results show that the CNN classification gives the better result with 97.9% classification accuracy according to the results of ANN. Thus, CNN emerges as the ideal classification tool for the classification of heart sounds with variable characteristics

Heartbeat murmurs detection in phonocardiogram recordings via transfer learning

Heart murmurs are abnormal heartbeat patterns that could be indicative of a serious heart condition, which can only be detected by trained specialists with the use of a stethoscope. However, it is occasionally the case that those specialists are not available, resulting in the need for a machine-automated system for murmur detection. Many methods might be used to produce such a system, one of which is the utilization of transfer learning. A recent machine learning method that saw popularity due to the little time it needs for training and the boosted accuracy it produces. This paper aims at testing the performance of transfer learning when detecting murmurs of the heart, by evaluating three transfer learning models, namely, VGG16, VGG19, and ResNet50, trained on a database of phonocardiogram (PCG) heartbeat recordings, i.e., PASCAL CHSC database. The data is cleansed, processed, and converted into images using two signal representation methods; Spectrograms and Mel Frequency Cepstral Coefficients (MFCCs). The paper compares the results of each model, using metrics of accuracy and loss, where the use of Spectrograms proved to yield the best results with 83.95%, 83.95%, and 87.65%, classification accuracy for VGG16, VGG19, and ResNet50, respectively. Based on the findings of the paper, it is evident that the Spectrogram-ResNet50 transfer learning pipeline could further facilitate the detection of heart murmurs with less time spent on training

Biomedical Signal and Image Processing

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of which are MATLAB based

Wrist-based Phonocardiogram Diagnosis Leveraging Machine Learning

With the tremendous growth of technology and the fast pace of life, the need for instant information has become an everyday necessity, more so in emergency cases when every minute counts towards saving lives. mHealth has been the adopted approach for quick diagnosis using mobile devices. However, it has been challenging due to the required high quality of data, high computation load, and high-power consumption. The aim of this research is to diagnose the heart condition based on phonocardiogram (PCG) analysis using Machine Learning techniques assuming limited processing power, in order to be encapsulated later in a mobile device. The diagnosis of PCG is performed using two techniques; 1. parametric estimation with multivariate classification, particularly discriminant function. Which will be explored at length using different number of descriptive features. The feature extraction will be performed using Wavelet Transform (Filter Bank). 2. Artificial Neural Networks, and specifically Pattern Recognition. This will also use decomposed version of PCG using Wavelet Transform (Filter Bank). The results showed 97.33% successful diagnosis using the first technique using PCG with a 19 dB Signal-to-Noise-Ratio. When the signal was decomposed into four sub-bands using a Filter Bank of the second order. Each sub-band was described using two features; the signal’s mean and covariance. Additionally, different Filter Bank orders and number of features are explored and compared. Using the second technique the diagnosis resulted in a 100% successful classification with 83.3% trust level. The results are assessed, and new improvements are recommended and discussed as part of future work.Teknologian valtavan kehittymisen ja nopean elämänrytmin myötä välittömästi saatu tieto on noussut jokapäiväiseksi välttämättömyydeksi, erityisesti hätätapauksissa, joissa jokainen säästetty minuutti on tärkeää ihmishenkien pelastamiseksi. Mobiiliterveys, eli mHealth, on yleisesti valjastettu käyttöön nopeaksi diagnoosimenetelmäksi mobiililaitteiden avulla. Käyttö on kuitenkin ollut haastavaa korkean datan laatuvaatimuksen ja suurten tiedonkäsittelyvaatimuksien, nopean laskentatehon ja sekä suuren virrankulutuksen vuoksi. Tämän tutkimuksen tavoitteena oli diagnosoida sydänsairauksia fonokardiogrammianalyysin (PCG) perusteella käyttämällä koneoppimistekniikoita niin, että käytettävä laskentateho rajoitetaan vastaamaan mobiililaitteiden kapasiteettia. PCG-diagnoosi tehtiin käyttäen kahta tekniikkaa 1. Parametrinen estimointi käyttäen moniulotteista luokitusta, erityisesti signaalien erotteluanalyysin avulla. Tätä asiaa tutkittiin syvällisesti käyttäen erilaisia tilastotieteellisesti kuvailevia piirteitä. Piirteiden irrotus suoritettiin käyttäen Wavelet-muunnosta ja suodatinpankkia. 2. Keinotekoisia neuroverkkoja ja erityisesti hahmontunnistusta. Tässä menetelmässä käytetään myös PCG-signaalin hajoitusta ja Wavelet-muunnos -suodatinpankkia. Tulokset osoittivat, että PCG 19dB:n signaali-kohina-suhteella voi johtaa 97,33% onnistuneeseen diagnoosiin käytettäessä ensimmäistä tekniikkaa. Signaalin hajottaminen neljään alikaistaan suoritettiin käyttämällä toisen asteen suodatinpankkia. Jokainen alikaista kuvattiin käyttäen kahta piirrettä: signaalin keskiarvoa ja kovarianssia, näin saatiin yhteensä kahdeksan ominaisuutta kuvaamaan noin yhden minuutin näytettä PCG-signaalista. Lisäksi tutkittiin ja verrattiin eriasteisia suodattimia ja piirteitä. Toista tekniikkaa käyttäen diagnoosi johti 100% onnistuneeseen luokitteluun 83,3% luotettavuustasolla. Tuloksia käsitellään ja pohditaan, sekä tehdään niistä johtopäätöksiä. Lopuksi ehdotetaan ja suositellaan käytettyihin menetelmiin uusia parannuksia jatkotutkimuskohteiksi.fi=vertaisarvioitu|en=peerReviewed

Biomedical Signal and Image Processing

First published in 2005, Biomedical Signal and Image Processing received wide and welcome reception from universities and industry research institutions alike, offering detailed, yet accessible information at the reference, upper undergraduate, and first year graduate level. Retaining all of the quality and precision of the first edition, Biomedical Signal and Image Processing, Second Edition offers a number of revisions and improvements to provide the most up-to-date reference available on the fundamental signal and image processing techniques that are used to process biomedical information. Addressing the application of standard and novel processing techniques to some of today’s principle biomedical signals and images over three sections, the book begins with an introduction to digital signal and image processing, including Fourier transform, image filtering, edge detection, and wavelet transform. The second section investigates specifically biomedical signals, such as ECG, EEG, and EMG, while the third focuses on imaging using CT, X-Ray, MRI, ultrasound, positron, and other biomedical imaging techniques. Updated and expanded, Biomedical Signal and Image Processing, Second Edition offers numerous additional, predominantly MATLAB, examples to all chapters to illustrate the concepts described in the text and ensure a complete understanding of the material. The author takes great care to clarify ambiguities in some mathematical equations and to further explain and justify the more complex signal and image processing concepts to offer a complete and understandable approach to complicated concepts

5.Uluslararası Öğrenciler Fen Bilimleri Kongresi Tam Metin Kitabı

Çevrimiçi (IX, 431 Sayfa; 26 cm.)

Implementation of Wavelet Transform Extrasystole Heart Sound With Convolution Method for Feature Extraction

25th Signal Processing and Communications Applications Conference (SIU) -- MAY 15-18, 2017 -- Antalya, TURKEYWOS: 000413813100164In this study, discrete wavelet transform (DWT) has been implemented using convolution method with purpose of feature extraction of extra systole heart disorder in heart sound database towards classification. Dual down sampling has been applied for DWT method and reflectance has been applied to avoid boundary effect. Approximation and detail coefficients that obtained using DWT implementation has been compared with coefficients that obtained using MATLAB program using regression analysis. As result of the regression, the mean error squared (RMSE) has been found ideally as 0.00002515 and the R-squared value of 1.Turk Telekom, Arcelik A S, Aselsan, ARGENIT, HAVELSAN, NETAS, Adresgezgini, IEEE Turkey Sect, AVCR Informat Technologies, Cisco, i2i Syst, Integrated Syst & Syst Design, ENOVAS, FiGES Engn, MS Spektral, Istanbul Teknik Uni

Implementation of Wavelet Transform Extrasystole Heart Sound With Convolution Method for Feature Extraction

25th Signal Processing and Communications Applications Conference, SIU 2017 -- 15 May 2017 through 18 May 2017 -- -- 128703In this study, discrete wavelet transform (DWT) has been implemented using convolution method with purpose of feature extraction of extra systole heart disorder in heart sound database towards classification. Dual down sampling has been applied for DWT method and reflectance has been applied to avoid boundary effect. Approximation and detail coefficients that obtained using DWT implementation has been compared with coefficients that obtained using MATLAB program using regression analysis. As result of the regression, the mean error squared (RMSE) has been found ideally as 0.00002515 and the R-squared value of 1. © 2017 IEEE