Automatic Signal Classification Research Articles

Диагностика сигналов ЭКГ на основе цифровой обработки и оптимизированной модели трансформера

Heartbeat disorders are considered one of the main maladies that cause mortality. Therefore, their precocious diagnosis via ECG signal is critical for introducing prompt therapy. The advanced automatic classification of ECG signals has the potential to save cardiologists a tremendous amount of time while simultaneously decreasing the chance of misdiagnosis. The dilemma of massive parameters is troubling the current methods of ECG signal classification. Most recent methods exhibit inadequate performance for diagnosing ECG signals in the inter-patient mode. In an attempt to deal with the above limitations, this study offers an innovative, efficient, and end-to-end model. The suggested model uses the optimized transformer framework to classify the heartbeats according to the "Association for the Advancement of Medical Instrumentation, AAMI," and obeys the inter-patient setting. We constructed an efficient architecture called the optimized network to substitute the Self Attention Unit (SAU) in the encoder part of the transformer model. The suggested model, which includes an optimized network, outperforms the SAU-based transformer model and requires fewer computations. A robust embedding architecture based on a Convolutional Neural Network (CNN) with a Squeeze and Excitation (SE) network-based attention scheme that has been used for weighting the Local Heartbeat Shape Pattern (LHSP) features is presented. The introduced model exceeds the state-of-the-art. An extensive test has been done to compare the achievements of the suggested model with those of the cardiologists. The results proved the closeness of their performances.

Automatic classification of seizure and seizure-free EEG signals based on phase space reconstruction features.

Epilepsy is a type of brain disorder triggered by an abrupt electrical imbalance of neuronal networks. An electroencephalogram (EEG) is a diagnostic tool to capture the underlying brain mechanisms and detect seizure onset in epileptic patients. To detect seizures, neurologists need to manually monitor EEG recordings for long periods, which is challenging and susceptible to errors depending on expertise and experience. Therefore, automatic identification of seizure and seizure-free EEG signals becomes essential. This study introduces a method based on the features extracted from the phase space reconstruction for classifying seizure and seizure-free EEG signals. The computed features are derived from the elliptical area and interquartile range of the Euclidean distance by varying percentage values of data points ranging from 50 to 100%. We consider two public datasets and evaluate these features in each EEG epoch that includes the healthy, interictal, preictal, and ictal stages of epileptic subjects, utilizing the K-nearest neighbor classifier for classification. Results show that the features have higher values during the seizure than the seizure-free EEG signals and healthy subjects. Furthermore, the proposed features can effectively discriminate seizure EEG signals from the seizure-free and normal subjects with 100% accuracy, sensitivity, and specificity in both datasets. Likewise, the classification between the preictal stage and seizure EEG signals attains 98% accuracy. Overall, the reconstructed phase space features significantly enhance the accuracy of detecting epileptic EEG signals compared with existing methods. This advancement holds great potential in assisting neurologists in swiftly and accurately diagnosing epileptic seizures from EEG signals.

Directional soundscapes in the Norwegian Seas observed with a coherent hydrophone array

Directional soundscaping is an efficient approach for examining marine ecosystems since it allows the study of living organisms, their behavior, and temporo-spatial interactions with other natural and man-made objects underwater, useful for ecosystem monitoring during offshore energy activities, maritime surveillance, and defense. A large aperture coherent hydrophone array was employed for remote sensing in the Norwegian and Barents Seas in Spring 2014. Extensive analysis have been conducted in post-processing of recorded hydrophone array data for automatic detection, bearing estimation, and classification of signals by different sound producers, such as whale calls, ship radiated noise, and fish sounds. Directional soundscaping through passive ocean acoustic waveguide remote sensing (POAWRS) provides bearing-time trajectories of signal detections that can be applied for geographical mapping. The received marine mammal sounds include vocalizations from baleen whales such as humpback, fin, and minke, and toothed whales, such as beluga, pilot, and sperm. We provide an insight to the vocalizations and behaviorial patterns of these whales in the Lofoten and Northern Finmark regions. The relative contributions of distinct sound sources, including whales, fish, and ships, to the directional soundscapes are quantified. We also examine their temporo-spatial dependences via geographic mapping of acoustic signals from distinct sound producers.

Positional multi-length and mutual-attention network for epileptic seizure classification

The automatic classification of epilepsy electroencephalogram (EEG) signals plays a crucial role in diagnosing neurological diseases. Although promising results have been achieved by deep learning methods in this task, capturing the minute abnormal characteristics, contextual information, and long dependencies of EEG signals remains a challenge. To address this challenge, a positional multi-length and mutual-attention (PMM) network is proposed for the automatic classification of epilepsy EEG signals. The PMM network incorporates a positional feature encoding process that extracts minute abnormal characteristics from the EEG signal and utilizes a multi-length feature learning process with a hierarchy residual dilated LSTM (RDLSTM) to capture long contextual dependencies. Furthermore, a mutual-attention feature reinforcement process is employed to learn the global and relative feature dependencies and enhance the discriminative abilities of the network. To validate the effectiveness PMM network, we conduct extensive experiments on the public dataset and the experimental results demonstrate the superior performance of the PMM network compared to state-of-the-art methods.

Artificial intelligence for automatic classification of needle EMG signals: A scoping review

ObjectiveThis scoping review provides an overview of artificial intelligence (AI), including machine and deep learning techniques, in the interpretation of clinical needle electromyography (nEMG) signals. MethodsA comprehensive search of Medline, Embase and Web of Science was conducted to find peer-reviewed journal articles. All papers published after 2010 were included. The methodological quality of the included studies was assessed with CLAIM (checklist for artificial intelligence in medical imaging). Results51 studies were identified that fulfilled the inclusion criteria. 61% used open-source EMGlab data set to develop models to classify nEMG signal in healthy, amyotrophic lateral sclerosis (ALS) and myopathy (25 subjects). Only two articles developed models to classify signals recorded at rest. Most articles reported high performance accuracies, but many were subject to bias and overtraining. ConclusionsCurrent AI-models of nEMG signals are not sufficient for clinical implementation. Suggestions for future research include emphasizing the need for an optimal training and validation approach using large datasets of clinical nEMG data from a diverse patient population. SignificanceThe outcomes of this study and the suggestions made aim to contribute to developing AI-models that can effectively handle signal quality variability and are suitable for daily clinical practice in interpreting nEMG signals.

Enhancing Microseismic Signal Classification in Metal Mines Using Transformer-Based Deep Learning

As microseismic monitoring technology gains widespread application in mine risk pre-warning, the demand for automatic data processing has become increasingly evident. One crucial requirement that has emerged is the automatic classification of signals. To address this, we propose a Transformer-based method for signal classification, leveraging the global feature extraction capability of the Transformer model. Firstly, the original waveform data were framed, windowed, and feature-extracted to obtain a 16 × 16 feature matrix, serving as the primary input for the subsequent microseismic signal classification models. Then, we verified the classification performance of the Transformer model compared with five microseismic signal classification models, including VGG16, ResNet18, ResNet34, SVM, and KNN. The experimental results demonstrate the effectiveness of the Transformer model, which outperforms previous methods in terms of accuracy, precision, recall, and F1 score. In addition, a comprehensive analysis was performed to investigate the impact of the Transformer model’s parameters and feature importance on outcomes, which provides a valuable reference for further enhancing microseismic signal classification performance.

Effects of Attack Interference on Radio Signal Classification

Signal recognition and classification are urgent issues in the field of radio monitoring. Although there have been many academic publications on the automatic modulation classification of radio signals, these studies have focused on how to use the new algorithms to increase signal recognition accuracy, and there is a lack of consideration of the effect of interference on classification accuracy. Based on publicly available datasets, RF datasets for Machine Learning, this paper uses the deep learning algorithm to generate an attack interference dataset and studies the impact of attack interference and noise on the classification accuracy of radio signals. The experimental results are as follows. Firstly, the presence of attack interference generally reduces the accuracy of radio signal classification. Secondly, the power of attack interference has a direct relationship with classification accuracy. Thirdly, under the condition of attack interference, the classification performance trends of the three open-source public datasets are consistent. This further illustrates how optimizing the modulation classification performance in the presence of interference is the key to applying the algorithm to practice.

UACNet: A universal automatic classification network for microseismic signals regardless of waveform size and sampling rate

In microseismic monitoring, various types of vibration events are often collected. Realizing the automatic identification of microseismic events in many suspected events is the basis of monitoring timeliness. However, due to the different sampling methods of microseismic data provided by different products, the data often contains different waveform sizes and sampling frequencies. This makes it difficult for existing approaches to be widely used in different projects without data preprocessing. In this paper, we propose the Universal Automatic Classification Network (UACNet), a deep learning approach that automatically identifies microseismic data in engineering without preprocessing. The UACNet model includes multiple convolution layers, adaptive average pooling layers, fully connected layers, and UAC blocks. UAC block is a residual structure with multiple convolutional layers and reset and update gates. The adaptive average pooling layer unifies the input size, and the UAC block functions as a feature extraction network to mine sufficient features from data. We test the proposed UACNet on engineering data and compare it with existing common and advanced methods. As a result, UACNet passed the ablation study, and the classification accuracy of UACNet is 95.62%, which is higher than 89.14% of CNN, 91.24% of ResNet, 91.04% of CapsNet, and 86.16% of RTFN, respectively. Moreover, the influence of waveform size, sampling rate, signal-to-noise ratios, and amplitude on the accuracy of UACNet is analyzed. The results show that UACNet can overcome the influence of these factors and truly realize automatic real-time classification of microseismic signals without preprocessing.

Feature Fusion Based on Graph Convolution Network for Modulation Classification in Underwater Communication.

Automatic modulation classification (AMC) of underwater acoustic communication signals is of great significance in national defense and marine military. Accurate modulation classification methods can make great contributions to accurately grasping the parameters and characteristics of enemy communication systems. While a poor underwater acoustic channel makes it difficult to classify the modulation types correctly. Feature extraction and deep learning methods have proven to be effective methods for the modulation classification of underwater acoustic communication signals, but their performance is still limited by the complex underwater communication environment. Graph convolution networks (GCN) can learn the graph structured information of the data, making it an effective method for processing structured data. To improve the stability and robustness of AMC in underwater channels, we combined the feature extraction and deep learning methods by fusing the multi-domain features and deep features using GCN. The proposed method takes the relationships among the different multi-domain features and deep features into account. Firstly, a feature graph was built using the properties of the features. Secondly, multi-domain features were extracted from the received signals and deep features were extracted from the signals using a deep neural network. Thirdly, we constructed the input of GCN using these features and the graph. Then, the multi-domain features and deep features were fused by the GCN. Finally, we classified the modulation types using the output of GCN by way of a softmax layer. We conducted the experiments on a simulated dataset and a real-world dataset, respectively. The results show that the AMC based on GCN can achieve a significant improvement in performance compared to the current state-of-the-art methods. Our approach is robust in underwater acoustic channels.

Automatic microseismic signal classification for mining safety monitoring using the WaveNet classifier

AbstractMicroseismic monitoring is a promising method for the safety monitoring of underground mines. However, it is crucial to isolate microseismic signals related to the collapse of a mine from others for successful monitoring because the monitoring system records various signals. Recently, deep learning‐based classification techniques have achieved high performance in such data classification problems. In this context, we develop an automatic signal classification technique using the modified WaveNet classifier. The main characteristic of the WaveNet structure is its ability to extract features at various frequencies from very long time‐series data, and such an advantage makes the WaveNet suitable for seismic data processing. The data imbalance problem coming from the safe condition of the monitoring target is solved by augmenting the training data with those acquired from another mine and employing class weighting. After training, an optimal classifier is chosen considering the loss function, accuracy and Fβ score. The optimal classifier shows very high accuracy and excellent performance for the test data prediction. Compared to the random forest model and another one‐dimensional convolutional neural network–based network, the suggested classifier has higher reliability in predicting microseismic signals. Even though the proposed WaveNet model has a much more complex structure than the random forest model, the actual application examples demonstrate that the proposed model achieves high efficiency without any preprocessing. The automatic signal classifier developed in this study can be directly applied to various safety monitoring problems, not only mines, to improve the efficiency and reliability of monitoring systems.

AUTOMATIC MODULATION CLASSIFICATION USING DEEP LEARNING POLAR FEATURE

The automatic modulation classification of signals is of great importance in modern communications, especially on cognitive radio. Several methods have been used in this field, the most important of which is the classification of modulation automatically using Deep Learning, where the methods depend on the convolution neural network, which is one of the Deep Learning networks, achieved high accuracy in classifying the modulation, so the proposed network depends on the type of deep learning CNN consisting of four blocks, each block contains a set of symmetric and asymmetric filters. The network also contains Max Pool. In this paper, the features extracted in phase-squaring and polar have been combined for the input, which helps in extending the input, that is, an increase in the features inside the network. It also contributes to improving the accuracy of classifying the higher-order modulation through the Polar plane. The dataset RadioML 2018.01A was adopted, which is used in the most recent research, where 11 types of modulation normal-class: (FM, GMSK, QPSK, BPSK, 0QPSK, AM-SSB-SC, 4ASK, AM-DSB-SC, 16QAM, 8PSK,00K) were taken. A simulation of which can be found in Matlab 2021. The proposed network achieved 100% classification accuracy when the signal-to-noise ratio is greater or equal to 2 dB for 11 types of modulation. The results of the paper were compared with modern networks Baseline network, Visual Geometry Group network, and Residual Neural network. The comparison showed the superiority of the proposed network over these networks, as the proposed network achieved an accuracy equal to 100% at SNR 2 dB while BL achieved an accuracy equal to 72% at SNR 2 dB, RN, and VGG almost reach 93% at SNR 2 dB.

Heartbeat classification based on single lead-II ECG using deep learning

The analysis and processing of electrocardiogram (ECG) signals is a vital step in the diagnosis of cardiovascular disease. ECG offers a non-invasive and risk-free method for monitoring the electrical activity of the heart that can assist in predicting and diagnosing heart diseases. The manual interpretation of the ECG signals, however, can be challenging and time-consuming even for experts. Machine learning techniques are increasingly being utilized to support the research and development of automatic ECG classification, which has emerged as a prominent area of study. In this paper, we propose a deep neural network model with residual blocks (DNN-RB) to classify cardiac cycles into six ECG beat classes. The MIT-BIH dataset was used to validate the model resulting in a test accuracy of 99.51%, average sensitivity of 99.7%, and average specificity of 98.2%. The DNN-RB method has achieved higher accuracy than other state-of-the-art algorithms tested on the same dataset. The proposed method is effective in the automatic classification of ECG signals and can be used for both clinical and out-of-hospital monitoring and classification combined with a single-lead mobile ECG device. The method has also been integrated into a web application designed to accept digital ECG beats as input for analyses and to display diagnostic results.

Atrial Fibrillation Detection Using Electrocardiogram Signal Input to LMD and Ensemble Classifier

Atrial fibrillation (AFib) is a type of heart arrhythmia, marked by an erratic and rapid contraction of the atria. Computer-aided diagnosis of AFib using electrocardiogram (ECG) sensor data may become a valuable tool in the detection and management of this common cardiac arrhythmia. In this letter, we present a new hybrid approach for the automatic classification of ECG signals using the local mean decomposition (LMD) and ensemble boosted trees classifier (EBTC). The LMD algorithm is employed to adaptively decompose the recorded ECG data into product functions (PFs). Four entropy-based features namely, log energy, sure, Shannon, and threshold entropy are computed from each PFs. The Kruskal-Wallis algorithm is employed to check the statistical significance of the obtained features and an EBTC is used for the screening of AFib episodes. The proposed technique achieved the highest classification accuracy of 92.33%, 90.33%, and 90.00% by classifying immediate terminating and non-terminating, terminates after one minute and non-terminating, immediate terminating and terminates after one minute AFib episodes, respectively. The presented method outperforms the existing machine learning-based approaches for detecting AFib using ECG data acquired from the publicly accessible AF Termination Challenge Database.

An automated detection of epileptic seizures EEG using CNN classifier based on feature fusion with high accuracy

BackgroundEpilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results.MethodsIn order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals.ResultsThe empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%.ConclusionThe proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG.

Distinguishing a planetary transit from false positives: a Transformer-based classification for planetary transit signals

ABSTRACT Current space-based missions, such as the Transiting Exoplanet Survey Satellite (TESS), provide a large database of light curves that must be analysed efficiently and systematically. In recent years, deep learning (DL) methods, particularly convolutional neural networks (CNN), have been used to classify transit signals of candidate exoplanets automatically. However, CNNs have some drawbacks; for example, they require many layers to capture dependencies on sequential data, such as light curves, making the network so large that it eventually becomes impractical. The self-attention mechanism is a DL technique that attempts to mimic the action of selectively focusing on some relevant things while ignoring others. Models, such as the Transformer architecture, were recently proposed for sequential data with successful results. Based on these successful models, we present a new architecture for the automatic classification of transit signals. Our proposed architecture is designed to capture the most significant features of a transit signal and stellar parameters through the self-attention mechanism. In addition to model prediction, we take advantage of attention map inspection, obtaining a more interpretable DL approach. Thus, we can identify the relevance of each element to differentiate a transit signal from false positives, simplifying the manual examination of candidates. We show that our architecture achieves competitive results concerning the CNNs applied for recognizing exoplanetary transit signals in data from the TESS telescope. Based on these results, we demonstrate that applying this state-of-the-art DL model to light curves can be a powerful technique for transit signal detection while offering a level of interpretability.

Classification of Impact Echo Signals Using Explainable Deep Learning and Transfer Learning Approaches

Impact echo (IE) is one of the most frequently used nondestructive evaluation (NDE) techniques for detecting subsurface defects such as delamination, honeycombing, and voids in concrete structures. In the conventional analysis of IE data, the time-domain signal is transformed into the frequency domain and the frequency content is used to estimate the presence and nature of the defect. Machine learning (ML) has been recently applied to the IE signal classification problem. However, because of the scarcity of labeled IE datasets, most existing work relies on relatively small training and test datasets without addressing the generalizability and transferability of the developed models. In this paper, we compare two approaches for automatic classification of IE signals: clustering based on expert-crafted features and deep learning (DL) from automatically extracted features. Next, we use the knowledge gained from a DL model trained on concrete specimens with available ground truth to make predictions about defects in a different specimen with completely different construction and characteristics (transfer learning). Finally, we examine our DL model to gain insights into the model working (explainability) and highlight the attributions that are significant in classifying a particular IE signal. Our findings demonstrate the utility of ML and DL for IE signal classification, but also highlight the need for high-quality labeled datasets for advancing ML and DL in NDE data analysis.

CANova: A hybrid intrusion detection framework based on automatic signal classification for CAN

Over the years, vehicles have become increasingly complex and an attractive target for malicious adversaries. This raised the need for effective and efficient Intrusion Detection Systemss (IDSs) for onboard networks able to work with the stringent requirements and the heterogeneity of information transmitted on the Controller Area Network. While state-of-the-art solutions are effective in detecting specific types of anomalies and work on a subset of the CAN signals, no single method can perform better than the others on all types of attacks, particularly if they need to provide predictions to comply with the domain’s real-time constraints. In this paper, we present CANova, a modular framework that exploits the characteristics of the different Controller Area Network (CAN) packets to select the Intrusion Detection Systemss (IDSs) that better fits them. In particular, it uses flow- and payload-based IDSs to analyze the packets’ content and arrival time. We evaluate CANova by comparing its performance against state-of-the-art Intrusion Detection Systemss (IDSs) for in-vehicle network and a comprehensive set of synthetic and real attacks in real-world CAN datasets. We demonstrate that our approach can achieve good performances in terms of detection, false positive rates, and temporal performances.

Automatic defect depth estimation for ultrasonic testing in carbon fiber reinforced composites using deep learning

Ultrasonic testing (UT) is commonly used to inspect the geometric shape of internal damage in composite materials and the test results need to be interpreted by trained experts. In this work, an automatic signal classification method based on deep learning is proposed for depth estimation of the detects introduced by low-velocity impact (LVI) in carbon fiber reinforced plastics (CFRPs). Three kinds of neural networks, LSTM, CNN, and CNN-LSTM are used to analyze the attributes with different depths. Then, trained models are applied to identify the depth information of impact damage. The results show that the CNN-LSTM model is a more accurate in-depth classification for LVI defects in CFRP based on A-scan signals than the other two structures.

Automated Damage Detection Using Lamb Wave-Based Phase-Sensitive OTDR and Support Vector Machines.

In this paper, we propose and demonstrate a damage detection technique based on the automatic classification of the Lamb wave signals acquired on a metallic plate. In the reported experiments, Lamb waves are excited in an aluminum plate through a piezoelectric transducer glued onto the monitored structure. The response of the monitored structure is detected through a high-resolution phase-sensitive optical time-domain reflectometer (ϕ-OTDR). The presence and location of a small perturbation, induced by placing a lumped mass of 5 g on the plate, are determined by processing the optical fiber sensor data through support vector machine (SVM) classifiers trained with experimental data. The results show that the proposed method takes full advantage of the multipoint sensing nature of the ϕ-OTDR technology, resulting in accurate damage detection and localization.

A Study on Classification Method of Mine Vibration Based on Microseismic Monitoring Cloud Service Platform

With the gradual deepening of mining depth, sudden ground pressure disasters such as rock burst and collapse caused by deep high stress and high rock pressure are major hidden dangers affecting mine safety production. Microseismic monitoring technology has been widely applied in the field of mine ground pressure disaster warning. The existing microseismic monitoring system has some problems, such as inaccurate automatic recognition and classification of waveform signals, low quality and low efficiency of manual processing. In this paper, the automatic classification of vibration signals in the process of mining is studied, and the automatic classification results are uploaded to the cloud service platform in real time, which solves the technical bottleneck of the existing microseismic monitoring system. Meanwhile, the real-time monitoring of mine ground pressure safety is guaranteed based on the cloud service platform of microseismic monitoring.