Personal identification system using physiological signal

This study explores the use of Hidden Markov Models (HMM) combined with a hybrid ensemble method using boosting techniques for the classification of different types of electrocardiogram (ECG) signals, including Normal, Ventricular Tachycardia (VTach), Ventricular Fibrillation (VFib), and Bradycardia. The analysis focuses on leveraging Auto Correlation and Partial Autocorrelation (PACF) for feature extraction and enhancing the classification performance through a hybrid approach integrating HMMs and boosting. First, Wavelet-based filtering was applied to remove noise from the ECG signals, providing cleaner data for subsequent feature extraction. Both Autocorrelation (AC) and Partial Autocorrelation (PACF) were computed for the filtered signals. While AC provided general periodicity information, PACF offered a more precise analysis by isolating direct correlations at each lag, which was especially useful for differentiating irregular rhythms like VTach and VFib. We then implemented a hybrid ensemble method combining Hidden Markov Models (HMMs) with boosting techniques, such as AdaBoost and Gradient Boosting, to improve classification accuracy. The HMMs were used to model the sequential dependencies in the ECG signals, while the boosting algorithms were applied to optimize the performance of the ensemble by weighting and improving weaker classifiers iteratively. The study demonstrates that the combination of HMMs, boosting, and PACF provides a powerful and efficient method for automatic arrhythmia detection, achieving high precision and robustness across different ECG signal types.

Power line noise disturbs and masks important data of ECG signal and whenever electrodes are attached to the body surface or when an exterior power source such as the sinusoidal 50 Hz/60 Hz signal interferes with the Electrocardiogram (ECG) signal. Hence it is necessary to remove this power line noise with appropriate signal processing. This paper presents the architecture of an LMS based Adaptive filter to minimize the low frequency Power line noise from the ECG. The system architecture is implemented on FPGA using Spartan 3s400pq208-4 board and Xilinx System Generator (XSG) software. The signal under experiments is added with low frequency Power line noise and is retrieved from MIT_BIH database. Efficient removal of low frequency Power line noise is helpful for decision making systems for saving the life of a person. The research work has been carried out by using the adaptive filter on ECG signal to remove low frequency Power line noise with preventing ECG signal.

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR.

Due to the capacity of processing signal with low energy consumption, compressive sensing (CS) has been widely used in wearable health monitoring system for arrhythmia classification of electrocardiogram (ECG) signals. However, most existing works focus on compressive sensing reconstruction, in other words, the ECG signals must be reconstructed before use. Hence, these methods have high computational complexity. In this paper, the authors propose a cardiac arrhythmia classification scheme that performs classification task directly in the compressed domain, skipping the reconstruction stage. The proposed scheme first employs the Pan–Tompkins algorithm to preprocess the ECG signals, including denoising and QRS detection, and then compresses the ECG signals by CS to obtain the compressive measurements. The features are extracted directly from these measurements based on principal component analysis (PCA), and are used to classify the ECG signals into different types by the proposed semi-supervised learning algorithm based on support vector machine (SVM). Extensive simulations have been performed to validate the effectiveness of the proposed scheme. Experimental results have shown that the proposed scheme achieves an average accuracy of [Formula: see text] at a sensing rate of 0.7, compared to an accuracy of [Formula: see text] for noncompressive ECG data.

Convolutional neural networks (CNNs) have lately proven to be extremely effective in image recognition. Besides CNN, hidden Markov chains (HMCs) are probabilistic models widely used in image processing. This paper presents a new hybrid model composed of both CNNs and HMCs. The CNN model is used for feature extraction and dimensionality reduction and the HMC model for classification. In the new model, named CNN-HMC, convolutional and pooling layers of the CNN model are applied to extract features maps. Also a Peano scan is applied to obtain several HMCs. Expectation–Maximization (EM) algorithm is used to estimate HMC’s parameters and to make the Bayesian Maximum Posterior Mode (MPM) classification method used unsupervised. The objective is to enhance the performances of the CNN models for the image classification task. To evaluate the performance of our proposal, it is compared to six models in two series of experiments. In the first series, we consider two CNN-HMC and compare them to two CNNs, 4Conv and Mini AlexNet, respectively. The results show that CNN-HMC model outperforms the classical CNN model, and significantly improves the accuracy of the Mini AlexNet. In the second series, it is compared to four models CNN-SVMs, CNN-LSTMs, CNN-RFs, and CNN-gcForests, which only differ from CNN-HMC by the second classification step. Based on five datasets and four metrics recall, precision, F1-score, and accuracy, results of these comparisons show again the interest of the proposed CNN-HMC. In particular, with a CNN model of 71% of accuracy, the CNN-HMC gives an accuracy ranging between 81.63% and 92.5%.

Now a day’s security and identification of a person are crucial in real time applications and solving the problems in biometric identification. Objectives: To address the issues in real time, the face and its organ’s identification are the main parts of the human body. The proposed work involves extraction of the face of the real time captured image, after extraction of the face, the organs like eyes, mouth and nose is extracted for identification of exact person. Methods/ Analysis: The present work captures the real time image from cameras or moving video devices for facial recognition using Principal Component Analysis (PCA), the facial features are extracted from both Hidden Markov Model (HMM), Gaussian mixture model (GMM) methods and classified into different organs using Artificial Neural Network (ANN). The features of organs are extracted in different stages, in first stage Eigen values with the help of PCA, second and third stage feature extraction with help GMM and HMM since these three techniques are the most powerful tools for statistical natural image processing. After extraction of organs of the face, the ANN is applied for classifications of eyes, mouth and nose separately. Findings: In the proposed work the facialorgans are separated into threeslight scale images and these are recombined to acquire the appreciationfacial image results like mouth, eyes and nose. The final obtained results shows that the proposed method has been achieved 95.8% recognition accuracy, Fault Rejection Ratio (FRR) is about 93.1% and Fault Acceptance Ratio (FAR) is 1.7 % which are implemented in Matlab2013A.

This paper considers various personal identification systems based on questions and answers concerning knowledge inherent to the individual, and discusses the personal identification probability. Assume a personal identification system which contains N knowledge items and assume that a solver has forgotten the correct answers for M items after the registration. The behavior of the system is as follows: Q knowledge items for questions are selected randomly from N items. For each of the selected items, R selection keys are presented which include a correct answer and the solver is requested to select one. When the solver forgets the correct answer, the probability of correct answer is 1/R; otherwise it is 1. The following three systems are considered under such conditions. System I decides the solver as correct if all answers to the extracted Q items are correct. System II decides the solver as correct if there are fewer than K incorrect answers. System III decides the solver as correct unless k or more consecutive answers are incorrect. It is seen that the probability of personal identification increases in the order of I, II and III. It is not always true that the probability of excluding an incorrect solver is low if the probability of personal identification is high. Each of the considered systems is suited to particular applications, depending on the environment.

A sole biometric identifier in construction an individual identification is often not able to meet the preferred performance requirements. Biometric identification pedestal on multiple biometrics correspond to an up-and-coming trend. computerized biometric scheme for person identification gauge a “signature” of the human body, compare the resulting characteristic to a database, and cause to be an request dependent decision. These biometric systems for personal authentication and identification are based upon physiological or behavioral features which are classically characteristic, even though point in time varying, such as Face recognition, Iris recognition, Fingerprint verification, Palm print verification in making a personal identification. Multi-biometric systems, which consolidate in sequence from multiple biometric sources, are in advance popularity since they are able to conquer limitations such as non-universality, noisy sensor data, large intra-user difference and vulnerability to spoof attacks that are commonly encountered in uni-biometric systems. In this paper, it addresses the concept issues and the applications strategies of multi-biometric systems.

Driver's drowsiness and fatigue have been major causes of the serious traffic accidents, which make this an area of great socioeconomic concern. This paper describes the design of ECG (Electrocardiogram) sensor with conductive fabric electrodes and PPG (Photoplethysmogram) sensor to obtain physiological signals for car driver's health condition monitoring. ECG and PPG signals are transmitted to base station connected to the server PC via personal area network for practical test. Intelligent health condition monitoring system is designed at the server to analyze the PPG and ECG signals. Our purpose for intelligent health condition monitoring system is managed to process HRV signals analysis derived from the physiological signals in time and frequency domain and to evaluate the driver's drowsiness status.

Electrocardiogram (ECG) are the physiological signals and a standard test to measure the heart’s electrical activity that depicts the movement of cardiac muscles. A review study has been conducted on ECG signals analysis with the help of artificial intelligence (AI) methods over the last ten years i.e., 2012–22. Primarily, the method of ECG analysis by software systems was divided into classical signal processing (e.g. spectrograms or filters), machine learning (ML) and deep learning (DL), including recursive models, transformers and hybrid. Secondly, the data sources and benchmark datasets were depicted. Authors grouped resources by ECG acquisition methods into hospital-based portable machines and wearable devices. Authors also included new trends like advanced pre-processing, data augmentation, simulations and agent-based modeling.The study found improvement in ECG examination perfection made each year through ML, DL, hybrid models, and transformers. Convolutional neural networks and hybrid models were more targeted and proved efficient. The transformer model extended the accuracy from 90% to 98%. The Physio-Net library helps acquire ECG signals, including the popular benchmark databases such as MIT-BIH, PTB, and challenging datasets. Similarly, wearable devices have been established as a appropriate option for monitoring patient health without the time and place limitations and are also helpful for AI model calibration with so far accuracy of 82%–83% on Samsung smartwatch. In the pre-processing signals, spectrogram generation through Fourier and wavelet transformations are erected leading approaches promoting on average accuracy of 90%–95%. Likewise, data enhancement using geometrical techniques is well-considered; however, extraction and concatenation-based methods need attention. As the what-if analysis in healthcare or cardiac issues can be performed using a complex simulation, the study reviews agent-based modeling and simulation approaches for cardiovascular risk event assessment.

Emotions play a significant role in human-computer interaction and entertainment consumption behavior, which young adults commonly use. The main challenge is the lack of a publicly available dataset for young adults with emotion labeling of physiological signals. This article presents a multi-modal data set of Electrocardiograms (ECG) and Galvanic Skin Response (GSR) signals for the emotion classification of young adults. Signal acquisition was performed through Shimmer3 ECG and Shimmer3 GSR units wearable to the chest and palm of the participants. The ECG signals were acquired from 25 participants, while GSR signals were acquired from 12 participants while watching 21 emotional stimulus videos divided into three sessions. The data was self-annotated for seven emotions: happy, sad, fear, surprise, anger, disgust, and neutral. These emotional states were further self-annotated with five very low, low, moderate, high, and very high-intensity levels of felt emotion. The participant also annotated valence, arousal, and dominance scores through Google form against each provided stimulus. The base experimental results for classifying four classes of high valence high arousal (HVHA), high valence low arousal (HVLA), low valence high arousal (LVHA), and low valence low arousal for ECG data is reported with an accuracy of 69.66%. Our baseline method for the proposed dataset achieved 66.64% accuracy for the eight-class classification of categorical emotions. The significance of data lies in the more emotional classes and less intrusive sensors to mimic real-world applications. Young adult's affective data (YAAD) is made publicly available, and it is valuable for researchers to develop behavioral assessments based on physiological signals.

Electrocardiogram(ECG) signal is one of the physiological signals which is used to measure health and disease detection of a human being. Extraction of QRS part from ECG signal is an important topic research from several years. Pan Tompkins method is a well known and widely used one for the QRS detection which consists of digital differentiator as one of the constituent part. In this paper, an attempt is made to study the working of the Pan Tompkins algorithm by replacing the digital differentiator block with the Infinite Impulse Response Digital differentiators. All simulations are carried out using MATLAB and the results obtained are comparable to the conventional method.

The modes in the electrocardiogram (ECG) signal can be divided into stable modes and unstable modes. The unstable modes are of great significance for signal analysis and classification. When the traditional dynamic mode decomposition (DMD) method is directly applied to these signals, it is difficult to effectively extract these key modes due to the rank mismatch problem of the data matrix. In order to better capture and analyze unstable modes, this study introduced the Hankel matrix to expand ECG data and used it as the input of DMD to propose the Hankel dynamic mode decomposition (HDMD) method. Although HDMD has been applied in other physiological signal processing, this study is the first to successfully apply it to multi-lead ECG signal analysis. By optimizing the delay parameters of the Hankel matrix and retaining the modes that account for 90% of the singular value decomposition energy, we significantly improve the effectiveness of feature extraction. Each type of abnormal ECG signal data is classified on the PTB Diagnostic ECG database. The experimental results of the proposed model are compared with the direct use of DMD for modal extraction. The highest classification accuracy of multi-modal ECG signal data extracted by HDMD is more than 10% higher than that using DMD. At the same time, the mean squared error (MSE) of the reconstruction using HDMD is 0.282 lower than that of DMD, indicating a significant improvement in reconstruction accuracy. Further illustrating the proposed method, the HDMD method can better capture and analyze the unstable modes in the ECG signal, thereby significantly improving the accuracy and robustness of signal classification. Future work will focus on further optimizing the parameter selection of the HDMD model and exploring its application potential in real-time heart disease monitoring and early warning systems.

Introduction: The electrocardiogram (ECG) signal is important for early diagnosis of heart abnormalities. Type 2 diabetic individuals’ ECG signals provide pertinent data about their heart and are one of the most important diagnostic techniques used by doctors to identify Cardiovascular Disease (CVD). Bidirectional Recurrent Neural Network (RNN) classifies the features linked to normal and abnormal stage ECG signal. Aim: To analyse ECG signals of type 2 diabetic patients for early prediction of CVDs using feature extraction and bidirectional RNN based classification. Materials and Methods: This was a secondary data-based modelling study at Shri Ramasamy Memorial University Sikkim, India from December 2020 to January 2022. Different noises were removed by hybrid preprocessing filter made up of a Median and Savitzky-Golay filter. Undecimated Dual Tree Complex Wavelet Transform (UDTCWT) along with Detrended fluctuation (DA) analysis and Empirical Orthogonal Function (EOF) analysis were then used to extract features. These features were classified with Bidirectional RNN. Results: The proposed method was tested on the MIT-BIH, Physionet and DICARDIA databases, and the findings showed that it achieves an average accuracy of 97.6% when compared to the conventional techniques. Conclusion: The proposed method proves to be the most effective way for detecting anomalies in ECG signals in both the early and pathological stages. This method is also effective to diagnose the early intervention of cardiovascular symptoms.

In this paper, we conduct an in-depth study and analysis of sports video recognition by improved hidden Markov model. The feature module is a complex gesture recognition module based on hidden Markov model gesture features, which applies the hidden Markov model features to gesture recognition and performs the recognition of complex gestures made by combining simple gestures based on simple gesture recognition. The combination of the two modules forms the overall technology of this paper, which can be applied to many scenarios, including some special scenarios with high-security levels that require real-time feedback and some public indoor scenarios, which can achieve different prevention and services for different age groups. With the increase of the depth of the feature extraction network, the experimental effect is enhanced; however, the two-dimensional convolutional neural network loses temporal information when extracting features, so the three-dimensional convolutional network is used in this paper to extract features from the video in time and space. Multiple binary classifications of the extracted features are performed to achieve the goal of multilabel classification. A multistream residual neural network is used to extract features from video data of three modalities, and the extracted feature vectors are fed into the attention mechanism network, then, the more critical information for video recognition is selected from a large amount of spatiotemporal information, further learning the temporal dependencies existing between consecutive video frames, and finally fusing the multistream network outputs to obtain the final prediction category. By training and optimizing the model in an end-to-end manner, recognition accuracies of 92.7% and 64.4% are achieved on the dataset, respectively.