22 research outputs found
Inter Patient Atrial Fibrillation Classification Using One Dimensional Convolution Neural Network
Atrial fibrillation is the most common type of arrhythmia. The process of detecting AF disease is quite difficult. This is because it is necessary to detect the presence or absence of a P signal wave in the ECG signal. However, this method requires special expertise from a cardiologist. Several literatures have proposed an automatic ECG classification system. However, the intra-patient paradigm does not simulate real-world scenarios. One of the challenges in the inter-patient paradigm is the morphological differences between one subject and another. In order to overcome the problems that arise in the automatic classification of ECG signal patterns a deep learning approach was proposed. This study proposes the classification process of atrial fibrillation in the inter-patient paradigm using a one-dimensional convolutional neural network architecture. The test is divided into two cases: two labels (Normal and AF) and three labels (Normal, AF and Non-AF). In the case of two test labels with an inter-patient scheme, the performance was 100% for all test metrics (accuracy, sensitivity, precision, and F1-Score). However, in the three-label case, the model's performance decreased to 85.95, 70.02, 72.50, 71.19 for accuracy, sensitivity, precision and F1-Score, respectively
Deep Learning Based Packet Detection and Carrier Frequency Offset Estimation in IEEE 802.11ah
Wi-Fi systems based on the IEEE 802.11 standards are the most popular
wireless interfaces that use Listen Before Talk (LBT) method for channel
access. The distinctive feature of a majority of LBT-based systems is that the
transmitters use preambles that precede the data to allow the receivers to
perform packet detection and carrier frequency offset (CFO) estimation.
Preambles usually contain repetitions of training symbols with good correlation
properties, while conventional digital receivers apply correlation-based
methods for both packet detection and CFO estimation. However, in recent years,
data-based machine learning methods are disrupting physical layer research.
Promising results have been presented, in particular, in the domain of deep
learning (DL)-based channel estimation. In this paper, we present a performance
and complexity analysis of packet detection and CFO estimation using both the
conventional and the DL-based approaches. The goal of the study is to
investigate under which conditions the performance of the DL-based methods
approach or even surpass the conventional methods, but also, under which
conditions their performance is inferior. Focusing on the emerging IEEE
802.11ah standard, our investigation uses both the standard-based simulated
environment, and a real-world testbed based on Software Defined Radios.Comment: 13 pages, journal publicatio
Speech Emotion Recognition using Supervised Deep Recurrent System for Mental Health Monitoring
Understanding human behavior and monitoring mental health are essential to
maintaining the community and society's safety. As there has been an increase
in mental health problems during the COVID-19 pandemic due to uncontrolled
mental health, early detection of mental issues is crucial. Nowadays, the usage
of Intelligent Virtual Personal Assistants (IVA) has increased worldwide.
Individuals use their voices to control these devices to fulfill requests and
acquire different services. This paper proposes a novel deep learning model
based on the gated recurrent neural network and convolution neural network to
understand human emotion from speech to improve their IVA services and monitor
their mental health.Comment: 6 pages, 5 figures, 3 tables, accepted in the IEEE WFIoT202
A new transformation for embedded convolutional neural network approach toward real-time servo motor overload fault-detection
Overloading in DC servo motors is a major concern in industries, as many
companies face the problem of finding expert operators, and also human
monitoring may not be an effective solution. Therefore, this paper proposed an
embedded Artificial intelligence (AI) approach using a Convolutional Neural
Network (CNN) using a new transformation to extract faults from real-time input
signals without human interference. Our main purpose is to extract as many as
possible features from the input signal to achieve a relaxed dataset that
results in an effective but compact network to provide real-time fault
detection even in a low-memory microcontroller. Besides, fault detection method
a synchronous dual-motor system is also proposed to take action in faulty
events. To fulfill this intention, a one-dimensional input signal from the
output current of each DC servo motor is monitored and transformed into a 3d
stack of data and then the CNN is implemented into the processor to detect any
fault corresponding to overloading, finally experimental setup results in
99.9997% accuracy during testing for a model with nearly 8000 parameters. In
addition, the proposed dual-motor system could achieve overload reduction and
provide a fault-tolerant system and it is shown that this system also takes
advantage of less energy consumption
A Comparative Study of Time and Frequency Domain Approaches to Deep Learning based Speech Enhancement
Deep learning has recently made a breakthrough in the speech enhancement process. Some architectures are based on a time domain representation, while others operate in the frequency domain; however, the study and comparison of different networks working in time and frequency is not reported in the literature. In this paper, this comparison between time and frequency domain learning for five Deep Neural Network (DNN) based speech enhancement architectures is presented. The comparison covers the evaluation of the output speech using four objective evaluation metrics: PESQ, STOI, LSD, and SSNR increase. Furthermore, the complexity of the five networks was investigated by comparing the number of parameters and processing time for each architecture. Finally some of the factors that affect learning in time and frequency were discussed. The primary results of this paper show that fully connected based architectures generate speech with low overall perception when learning in the time domain. On the other hand, convolutional based designs give acceptable performance in both frequency and time domains. However, time domain implementations show an inferior generalization ability. Frequency domain based learning was proved to be better than time domain when the complex spectrogram is used in the training process. Additionally, feature extraction is also proved to be very effective in DNN based supervised speech enhancement, whether it is performed at the beginning, or implicitly by bottleneck layer features. Finally, it was concluded that the choice of the working domain is mainly restricted by the type and design of the architecture used
Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification
Artificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs
The importance of acoustic background modelling in CNN-based detection of the neotropical White-lored Spinetail (Aves, Passeriformes, Furnaridae)
Machine learning tools are widely used in support of bioacoustics studies, and there are numerous publications on the applicability of convolutional neural networks (CNNs) to the automated presence-absence detection of species. However, the relation between the merit of acoustic background modelling and the recognition performance needs to be better understood. In this study, we investigated the influence of acoustic background substance on the performance of the acoustic detector of the White-lored Spinetail (Synallaxis albilora). Two detector designs were evaluated: the 152-layer ResNet with transfer learning and a purposely created CNN. We experimented with acoustic background representations trained with season-specific (dry, wet, and all-season) data and without explicit modelling to evaluate its influence on the detection performance. The detector permits monitoring of the diel behaviour and breeding time of White-lored Spinetail solely based on the changes in the vocal activity patterns. We report an advantageous performance when background modelling is used, precisely when trained with all-season data. The highest classification accuracy (84.5%) was observed for the purposely created CNN model. Our findings contribute to an improved understanding of the importance of acoustic background modelling, which is essential for increasing the performance of CNN-based species detectors.This work was supported by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brazil (CAPES) under Grant [CAPES-01]; Instituto Nacional de Ciência e Tecnologia em Áreas Úmidas (INAU/UFMT/CNPq); Centro de Pesquisa do Pantanal (CPP); and Brehm Funds for International Bird Conservation (BF), Germany
Prediction of Preoperative Scale Score of Dystonia Based on Few-Shot Learning
As a neurological disease, dystonia mainly has symptoms including muscle stiffness, dyskinesia, tremor, muscle spasm, etc. Dystonia score plays an important role in targeted auxiliary diagnosis, treatment plan design, and follow-up evaluation of patients. In this paper, the feature information of brain lateralization is extracted from electroencephalography (EEG) signals by clustering method, while information on time domain, frequency domain, and time sequence are extracted from EEG signals and electromyography (EMG) signals. Various deep-learning models are used to predict dystonia scores. Experiments show that this method can effectively predict dystonia based on the quantitative indicators extracted from few-shot neural signals. The methodology in this paper can help doctors judge the disease more accurately, make personalized treatment plans, and assist in monitoring the treatment effect
Global ECG Classification by Self-Operational Neural Networks with Feature Injection
Objective: Global (inter-patient) ECG classification for arrhythmia detection
over Electrocardiogram (ECG) signal is a challenging task for both humans and
machines. The main reason is the significant variations of both normal and
arrhythmic ECG patterns among patients. Automating this process with utmost
accuracy is, therefore, highly desirable due to the advent of wearable ECG
sensors. However, even with numerous deep learning approaches proposed
recently, there is still a notable gap in the performance of global and
patient-specific ECG classification performances. This study proposes a novel
approach to narrow this gap and propose a real-time solution with shallow and
compact 1D Self-Organized Operational Neural Networks (Self-ONNs). Methods: In
this study, we propose a novel approach for inter-patient ECG classification
using a compact 1D Self-ONN by exploiting morphological and timing information
in heart cycles. We used 1D Self-ONN layers to automatically learn
morphological representations from ECG data, enabling us to capture the shape
of the ECG waveform around the R peaks. We further inject temporal features
based on RR interval for timing characterization. The classification layers can
thus benefit from both temporal and learned features for the final arrhythmia
classification. Results: Using the MIT-BIH arrhythmia benchmark database, the
proposed method achieves the highest classification performance ever achieved,
i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N)
segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the
supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10%
recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs)