Biometric Authentication Using the Unique Characteristics of the ECG Signal

ECG is a biological signal specific for each person that is hard to create artificially. Therefore, its usage in biometry is highly investigated. It may be assumed that in the future, ECG for biometric purposes will be measured by wearable devices. Therefore, the quality of the acquired data will be worse compared to ambulatory ECG. In this study, we proposed and tested three different ECG-based authentication methods on data measured by Maxim Integrated wristband. Specifically, 29 participants were involved. The first method extracted 22 time-domain features – intervals and amplitudes from each heartbeat and Hjorth descriptors of an average heartbeat. The second method used 320 features extracted from the wavelet domain. For both methods a random forest was used as a classifier. The deep learning method was selected as the third method. Specifically, the 1D convolutional neural network with embedded feed-forward neural network was used to classify the raw signal of every heartbeat. The first method reached an average false acceptance rate (FAR) 7.11% and false rejection rate (FRR) 6.49%. The second method reached FAR 6.96% and FRR 21.61%. The third method reached FAR 0.57% and FRR 0.00%

Determinación de Transitorios en Sistemas Físicos usando la Transformada Discreta Wavelet

La información que se puede obtener directamente de la representación tiempo-amplitud de un fenómeno cualquiera no siempre es la más apropiada, puesto que la información que caracteriza a la señal, en muchos casos, puede observarse más claramente en el dominio de la frecuencia, es decir, mediante un espectro de frecuencias que muestre las frecuencias existentes en la señal. Para encontrar el contenido de frecuencia de diversas señales físicas se puede hacer uso de la transformada de Fourier (TF) ámpliamente conocida. La TF entrega la información en frecuencia de la señal, pero no indica el instante de tiempo en el que aparece; esta información no es necesaria cuando la señal es estacionaria; sin embargo es de crucial importancia para señales no estacionarias. Aunque la TF es una de las técnicas más empleadas, no es la única, hay muchas otras transformadas que se emplean, como la transformada Hilbert, la transformada rápida de Fourier (STFT), la distribución Wigner y la transformada Wavelet. Cada una de estas transformaciones tiene su propia área de aplicación, con ventajas y desventajas. En nuestro caso utilizaremos con exclusividad la Transformada Wavelet (TW) para estudiar situaciones no estacionarias, es decir las componentes de frecuencia cambian con el tiempo.Eje: Procesamiento de Señales y Sistemas de Tiempo RealRed de Universidades con Carreras en Informática (RedUNCI

A database of simultaneously recorded ECG signals with and without EMG noise

Goal: Noise on recorded electrocardiographic (ECG) signals may affect their clinical interpretation. Electromyographic (EMG) noise spectrally coincides with the QRS complex, which makes its removal particularly challenging. The problem of evaluating the noise-removal techniques has commonly been approached by algorithm testing on the contaminated ECG signals constructed ad hoc as an additive mixture of a noise-free ECG signal and noise. Consequently, there is an absence of a unique/standard database for testing and comparing different denoising methods. We present a SimEMG database recorded by a novel acquisition method that allows for direct recording of the genuine EMG-noise-free and -contaminated ECG signals. The database is available as open source

Wavelet Based Filtering of Electrocardiograms

Tato dizertační práce pojednává o možnostech využití vlnkových transformací pro odstranění širokopásmového svalového rušení v signálech EKG. V práci jsou nejprve rozebrány vlastnosti signálů EKG a především nejčastěji vyskytující se typy rušení. Dále je představena teorie vlnkových transformací a ukázány návrhy jednoduchého vlnkového filtru i sofistikovanější varianty využívající wienerovské filtrace vlnkových koeficientů. Další část práce je věnována návrhu vlastního filtru, který vychází právě z wienerovské vlnkové filtrace a je doplněn algoritmy zajišťujícími plnou adaptibilitu jeho parametrů při změně vlastností vstupního signálu. Vhodné parametry navrženého systému jsou hledány automatickým způsobem a algoritmus je testován na kompletní standardní databázi elektrokardiogramů CSE, kde dosahuje výrazně lepších výsledků než další srovnávané publikované metody.This dissertation deals with possibilities of using wavelet transforms for elimination of broadband muscle noise in ECG signals. In this work, the characteristics of ECG signals and particularly the most frequently occurring type of interference are discussed firstly. The theory of wavelet transforms is also introduced and followed by design of the simple wavelet filter and the more sophisticated version with wiener filtering of wavelet coefficients. Next part is devoted to the design of our filter, which is based on wavelet wiener filtering and is complemented by algorithms that ensure full adaptability of its parameters when the properties of the input signal are changing. Suitable parameters of the proposed system are searched automatically and the algorithm is tested on the complete standard electrocardiograms database CSE, where it achieves significantly better results than other published methods.

Implementation of Supervised Machine Learning on Embedded Raspberry Pi System to Recognize Hand Motion as Preliminary Study for Smart Prosthetic Hand

EMG signals have random, non-linear, and non-stationary characteristics that require the selection of the suitable feature extraction and classifier for application to prosthetic hands based on EMG pattern recognition. This research aims to implement EMG pattern recognition on an embedded Raspberry Pi system to recognize hand motion as a preliminary study for a smart prosthetic hand. The contribution of this research is that the time domain feature extraction model and classifier machine can be implemented into the Raspberry Pi embedded system. In addition, the machine learning training and evaluation process is carried out online on the Raspberry Pi system. The online training process is carried out by integrating EMG data acquisition hardware devices, time domain features, classifiers, and motor control on embedded machine learning using Python programming. This study involved ten respondents in good health. EMG signals are collected at two lead flexor carpi radialis and extensor digitorum muscles. EMG signals are extracted using time domain features (TDF) mean absolute value (MAV), root mean square (RMS), variance (VAR) using a window length of 100 ms. Supervised machine learning decision tree (DT), support vector machine (SVM), and k-nearest neighbor (KNN) are chosen because they have a simple algorithm structure and less computation. Finally, the TDF and classifier are embedded in the Raspberry Pi 3 Model B+ microcomputer. Experimental results show that the highest accuracy is obtained in the open class, 97.03%. Furthermore, the additional datasets show a significant difference in accuracy (p-value <0.05). Based on the evaluation results obtained, the embedded system can be implemented for prosthetic hands based on EMG pattern recognition

A morphology-preserving algorithm for denoising of EMG-contaminated ECG signals

Goal: Clinical interpretation of an electrocardiogram (ECG) can be detrimentally affected by noise. Removal of the electromyographic (EMG) noise is particularly challenging due to its spectral overlap with the QRS complex. The existing EMG-denoising algorithms often distort signal morphology, thus obscuring diagnostically relevant information. Methods: Here, a new iterative regeneration method (IRM) for efficient EMG-noise suppression is proposed. The main hypothesis is that the temporary removal of the dominant ECG components enables extraction of the noise with the minimum alteration to the signal. The method is validated on SimEMG database of simultaneously recorded reference and noisy signals, MIT-BIH arrhythmia database and synthesized ECG signals, both with the noise from MIT Noise Stress Test Database. Results: IRM denoising and morphology-preserving performance is superior to the wavelet- and FIR-based benchmark methods. Conclusions : IRM is reliable, computationally non-intensive, fast and applicable to any number of ECG channels recorded by mobile or standard ECG devices

Wavelet Filtering of ECG Signals

Práce se zabývá problematikou vlnkových transformací se zaměřením na vlnkové transformace s diskrétním časem (DTWT). Praktická část je zaměřena na realizaci redundantní paketové DTWT a její využití při filtraci signálů EKG. Stěžejní částí práce je navržení wienerovského filtru, využívající redundantní paketovou DTWT, který slouží k odstranění rušení myopotenciálů ze signálů EKG. Samotné řešení je realizováno v programu Matlab. Testování probíhá na knihovně CSE s využitím modelu šumu myopotenciálů, který slouží k zarušení originálních signálů. Pro optimální nastavení parametrů navrženého filtru je využito genetického algoritmu (GA). V práci je realizováno porovnání navrženého filtru s redundantní paketovou DTWT s variantou redundantní dyadické DTWT.The work deals with the wavelet transformation, focusing on wavelet transforms with discrete time (DTWT). The practical part is focused on the implementation of redundant packet DTWT and its use in the filtration of ECG signals. The main part of the work is to design wiener filter that uses redundant packet DTWT, designed to eliminate interference myopotentials of ECG signals. The actual solution is implemented in Matlab. Testing is performed on the library CSE using noise model myopotentials used to noising original signals. For optimum parameters designed filter is used the genetic algorithm (GA). The work is carried out comparing the proposed filter redundant packet DTWT a variant of redundant dyadic DTWT.

A Discrete Curvature Estimation Based Low-Distortion Adaptive Savitzky–Golay Filter for ECG Denoising

Electrocardiogram (ECG) sensing is an important application for the diagnosis of cardiovascular diseases. Recently, driven by the emerging technology of wearable electronics, massive wearable ECG sensors are developed, which however brings additional sources of noise contamination on ECG signals from these wearable ECG sensors. In this paper, we propose a new low-distortion adaptive Savitzky-Golay (LDASG) filtering method for ECG denoising based on discrete curvature estimation, which demonstrates better performance than the state of the art of ECG denoising. The standard Savitzky-Golay (SG) filter has a remarkable performance of data smoothing. However, it lacks adaptability to signal variations and thus often induces signal distortion for high-variation signals such as ECG. In our method, the discrete curvature estimation is adapted to represent the signal variation for the purpose of mitigating signal distortion. By adaptively designing the proper SG filter according to the discrete curvature for each data sample, the proposed method still retains the intrinsic advantage of SG filters of excellent data smoothing and further tackles the challenge of denoising high signal variations with low signal distortion. In our experiment, we compared our method with the EMD-wavelet based method and the non-local means (NLM) denoising method in the performance of both noise elimination and signal distortion reduction. Particularly, for the signal distortion reduction, our method decreases in MSE by 33.33% when compared to EMD-wavelet and by 50% when compared to NLM, and decreases in PRD by 18.25% when compared to EMD-wavelet and by 25.24% when compared to NLM. Our method shows high potential and feasibility in wide applications of ECG denoising for both clinical use and consumer electronics

Optimaalisten suotimien käyttö fysiologisten signaalien suodatuksessa

Elektroenkefalografia (EEG) ja elektrokardiografia (EKG) ovat tavallisia tutkimusmenetelmiä aivojen ja sydämen toiminnan tutkimisessa. Niistä saatavista signaaleista tarkastellaan aaltomuotoja ja niiden kestoja, amplitudeja sekä tiettyjen taajuusalueiden esiintymistä. Ne ovat kuitenkin alttiitta biologisille ja teknisille häiriöille, jotka vaikeuttavat signaalien tulkintaa ja voivat johtaa diagnoosivirheisiin. Häiriöitä pyritään suodattamaan vahingoittamatta signaalia. Tämän kirjallisuuskatsauksen tavoitteena on selvittää, miten optimaalisiin suotimiin kuuluvaa Wiener-suodinta käytetään EEG- ja EKG-signaalien suodatuksessa, millaisia tuloksia sillä on saatu, ja liittyykö siihen mahdollisesti jotain haasteita. Wiener-suodin hyödyntää tietoa puhtaasta signaalista, jota ei usein ole suoraan saatavilla. Kirjallisuudessa on esitetty useita eri tapoja selvittää puhtaan signaalin ominaisuuksia. Osa esitellyistä tavoista hyödyntää myös muita menetelmiä puhtaan signaalin löytämiseen. Tässä työssä tarkasteltiin sekä menetelmiä, joissa Wiener-suodinta on käytetty yksin, että menetelmiä, joissa Wiener-suotimeen on yhdistetty yksi tai useampia muita menetelmiä. Yhdistelmien tarkoituksena on saada enemmän ja tarkempaa tietoa puhtaasta signaalista, jotta suodin toimisi mahdollisimman hyvin. Yhdistelmillä voidaan myös vaikuttaa suotimen käyttömahdollisuuksiin. Kirjallisuudessa saatujen tulosten perusteella Wiener-suotimella saavutetaan samaa luokkaa olevia tuloksia kuin useilla muilla EEG- ja EKG-signaalien suodatukseen soveltuvilla menetelmillä. Yksiselitteisen paremmuusjärjestyksen muodostaminen on kuitenkin haastavaa, sillä tulokset vaihtelevat käytetyn signaalin ja suodatettavan häiriötyypin mukaan. Wiener-suotimella saavutetut tulokset eivät kuitenkaan ole selkeästi parhaita. Useamman menetelmän yhdistelmillä suodatusteho on parantunut verrattuna perinteiseen Wiener-suotimeen tai muihin vertailussa olleisiin menetelmiin. Tämä on osoitus siitä, että Wiener-suotimen suodatuskyky paranee, kun puhtaasta signaalista on enemmän tietoa