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Mental fatigue recognition study based on 1d convolutional neural network and short-term ECG signals

TECHNOLOGY ANd HEALTH CARE 2024年第5期32卷 3409-3422页

作者： Chen, Ruijuan Wang, Rui Fei, Jieying Huang, Lengjie Bi, Xun Wang, Jinhai Tiangong Univ Sch Life Sci Tianjin 300387 Peoples R China Tiangong Univ Sch Elect & Informat Engn Tianjin Peoples R China Chinese Peoples Armed Police Force Specialty Med C Mil Med Examinat & Certificat Sect Tianjin Peoples R China

BACKGROUNd: Mental fatigue has become a non-negligible health problem in modern life, as well as one of the important causes of social transportation, production and life accidents. OBJECTIVE: Fatigue detection based on traditional machine learning requires manual and tedious feature extraction and feature selection engineering, which is inefficient, poor in real-time, and the recognition accuracy needs to be improved. In order to recognize daily mental fatigue level more accurately and in real time, this paper proposes a mental fatigue recognition model based on 1d convolutional neural network (1d-CNN), which inputs 1d raw ECG sequences of 5 s duration into the model, and can directly output the predicted fatigue level labels. METHOdS: The fatigue dataset was constructed by collecting the ECG signals of 22 subjects at three time periods: 9:00-11:00 a.m., 14:00-16:00 p.m., and 19:00-21:00 p.m., and then inputted into the 19-layer 1d-CNN model constructed in the present study for the classification of mental fatigue in three grades. RESULTS: The results showed that the model was able to recognize the fatigue levels effectively, and its accuracy, precision, recall, and F1 score reached 98.44%, 98.47%, 98.41%, and 98.44%, respectively. CONCLUSION: This study further improves the accuracy and real-time performance of recognizing multi-level mental fatigue based on electrocardiography, and provides theoretical support for real-time fatigue monitoring in daily life.

关键词： Mental fatigue short-time electrocardiographic sequence deep learning 1d convolutional neural network

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Explainable Multistage Ensemble 1d convolutional neural network for Trust Worthy Credit decision

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INTERNATIONAL JOURNAL OF AdVANCEd COMPUTER SCIENCE ANd APPLICATIONS 2024年第2期15卷 351-358页

作者： Pavitha, N. Sugave, Shounak Dr Vishwanath Karad MIT World Peace Univ Pune Dept Comp Engn & Technol Pune Maharashtra India

Banking is a dynamic industry that places significant importance on risk management, requiring accurate and interpretable AI models to make transparent lending decisions. This study introduces a groundbreaking approach that combines a multistage ensemble technique with a 1d convolutional neural network (CNN) architecture. The algorithm not only delivers superior classification performance but also offers interpretable explanations for its decisions. The algorithm is designed with multiple strategic steps to enhance model performance without sacrificing explainability. Thorough experiments were conducted using datasets from private banks and non-banking financial companies ( NBFCs) in India to evaluate the algorithm's performance. It was compared against various state-of-the-art models, demonstrating remarkable precision, recall, F1 score, and accuracy values of 0.994, 0.992, 0.993, and 0.991, respectively. This outperformed competing models like homogeneous deep ensembles, 1d CNN, and Artificial neural networks (ANN). Furthermore, individual borrower dataset evaluations confirmed the proposed algorithm's consistency and efficiency, achieving precision, recall, F1 score, and accuracy values of 0.960, 0.961, 0.952, and 0.964, respectively. The research emphasizes the effectiveness of the explanatory integration decision process, wherein the Explainable Multistage Ensemble 1d CNN not only provides enhanced credit risk prediction but also facilitates transparent and interpretable lending decisions. The algorithm's ability to offer understandable explanations empowers financial institutions to make well-informed lending decisions, reduce credit risk, and foster a more stable and inclusive financial ecosystem.

关键词： Credit risk prediction explainable AI multistage ensemble 1d convolutional neural network interpretability transparency lending decisions financial institutions

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ComSense-CNN: acoustic event classification via 1d convolutional neural network with compressed sensing

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SIGNAL IMAGE ANd VIdEO PROCESSING 2023年第3期17卷 735-741页

作者： Tan, Pooi Shiang Lim, Kian Ming Tan, Cheah Heng Lee, Chin Poo Kwek, Lee Chung Multimedia Univ Fac Informat & Technol Jalan Ayer Keroh Lama Bukit Beruang 75450 Melaka Malaysia Motorola Solut Malaysia Sdn Bhd 24 Medan Bayan Lepas Bayan Lepas 11900 Pulau Pinang Malaysia Multimedia Univ Fac Engn & Technol Jalan Ayer Keroh Lama Bukit Beruang 75450 Melaka Malaysia

Sound plays an important role in human daily life as humans use sound to communicate with each other and to understand the events occurring in the surroundings. This has prompted the researchers to further study on how to automatically identify the event that is happening by analyzing the acoustic signal. This paper presents a deep learning model enhanced by compressed sensing techniques for acoustic event classification. The compressed sensing first transforms the input acoustic signal into a reconstructed signal to reduce the noise in the input acoustic signal. The reconstructed signals are then fed into a 1-dimensional convolutional neural network (1d-CNN) to train a deep learning model for the acoustic event classification. In addition, the dropout regularization is leveraged in the 1d-CNN to mitigate the overfitting problems. The proposed compressed sensing with 1d-CNN was evaluated on three benchmark datasets, namely Soundscapes1, Soundscapes2, and UrbanSound8K, and achieved F1-scores of 80.5%, 81.1%, and 69.2%, respectively.

关键词： Acoustic event classification Compressed sensing 1d convolutional neural network 1d-CNN

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1d convolutional neural network-based adaptive algorithm structure with system fault diagnosis and signal feature extraction for noise and vibration enhancement in mechanical systems

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MECHANICAL SYSTEMS ANd SIGNAL PROCESSING 2023年第1期197卷

作者： Hong, dongwoo Kim, Byeongil Yeungnam Univ Sch Mech Engn Gyongsan 38541 South Korea

A variety of adaptive algorithms are utilized to control the vibration and noise for mechanical systems in the industrial field, since their characteristics usually changes due to unwanted disturbance and malfunctioning. A reference signal plays an important role in signal tracking through adaptive algorithms, and well-characterized information about the tracked signal should be employed. However, it is typically difficult to determine the signal feature because operating systems generate complex signals. Moreover, when the system state changes due to malfunctions or disturbances, signal feature can also be changed, affecting the performance of adaptive algo-rithms. This study proposes a novel strategy based on a 1d convolutional neural network (1d CNN) for improving the signal tracking performance. 1d CNN has the ability to deal with consecutive data that is directly measured by an accelerometer attached to mechanical systems, as well as to extract the signal feature through convolutional layers. The development of proposed algorithm can be explained with following three parts: 1) data measurement opportunities are limited, particularly because fault data is more difficult to measure than normal state data. Therefore, when performing learning, there is a problem of a lack of training data. Thus, a data generator is proposed that is based on a generative adversarial network (GAN) and a variational auto encoder (VAE). 2) In order to extract the frequency and phase and consider the system state to automatically define the reference signal and increase tracking robustness, the methodology of signal feature extraction and diagnosis is proposed based on a 1d CNN. In order to validate the tracking performance, a neural network-based signal tracking algorithm is applied to the devel-oped structure. Furthermore, bearing and linear motion (LM) guide data from experiments is employed to confirm the versatility of the developed algorithm. Through above process, it is demonstra

关键词： 1d convolutional neural network data generator Signal feature extraction Signal tracking Linear motion guide

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Robust R-Peak detection in Low-Quality Holter ECGs Using 1d convolutional neural network

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IEEE TRANSACTIONS ON BIOMEdICAL ENGINEERING 2022年第1期69卷 119-128页

作者： Zahid, Muhammad Uzair Kiranyaz, Serkan Ince, Turker devecioglu, Ozer Can Chowdhury, Muhammad E. H. Khandakar, Amith Tahir, Anas Gabbouj, Moncef Qatar Univ Coll Engn Elect Engn Doha Qatar Izmir Univ Econ Elect & Elect Engn Dept Izmir Turkey Tampere Univ Dept Comp Sci Tampere 33101 Finland

Objective: Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Methods: In this study, a novel implementation of the 1d convolutional neural network (CNN) is used integrated with a verification model to reduce the number of false alarms. This CNN architecture consists of an encoder block and a corresponding decoder block followed by a sample-wise classification layer to construct the 1d segmentation map of R-peaks from the input ECG signal. Once the proposed model has been trained, it can solely be used to detect R-peaks possibly in a single channel ECG data stream quickly and accurately, or alternatively, such a solution can be conveniently employed for real-time monitoring on a lightweight portable device. Results: The model is tested on two open-access ECG databases: The China Physiological Signal Challenge (2020) database (CPSC-dB) with more than one million beats, and the commonly used MIT-BIH Arrhythmia database (MIT-dB). Experimental results demonstrate that the proposed systematic approach achieves 99.30% F1-score, 99.69% recall, and 98.91% precision in CPSC-dB, which is the best R-peak detection performance ever achieved. Results also demonstrate similar or better performance than most competing algorithms on MIT-dB with 99.83% F1-score, 99.85% recall, and 99.82% precision. Significance: Compared to all competing methods, the proposed approach can reduce the false-positives and false-negatives in Holter ECG signals by more than 54% and 82%, respectively. Conclusion: Finally, the simple and invariant nature of the parameters leads to a highly generic system and therefore applicab

关键词： Electrocardiography Sensitivity Performance evaluation Monitoring Benchmark testing Noise measurement Electronic mail 1d convolutional neural network R-peak detection ECG monitoring holter registers

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A SQL Injection Attack Recognition Model Based on 1d convolutional neural network 7th

A SQL Injection Attack Recognition Model Based on 1D Convolu...

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7th International Symposium on Artificial Intelligence and Robotics (ISAIR)

作者： Jiang, Jing Xu, Menghan Pan, Sen Zhu, Lipeng State Grid Smart Grid Res Inst Co Ltd Beijing 102200 Peoples R China State Grid Jiangsu Elect Power Co Ltd Infomat & Telecommun Branch Nanjing 210024 Peoples R China

ISBN: (纸本)9789811979453;9789811979460

The rapid development of Internet technology and the widespread popularization of web applications have brought a lot of convenience to people's lives. At the same time, due to the interaction of more user information between browsers and servers, attacks on web applications have intensified, and network security issues occur frequently. SQL injection attacks have always been a common tactic of cyber attackers due to their simplicity and high threat level. Nowadays, SQL injection attacks are emerging in an endless stream, and detection models based on traditional machine learning algorithms have been unable to identify effectively and accurately complex SQL injection attacks. In this paper, we propose a SQL injection recognition model based on one-dimensional convolutional neural network (1d CNN) and combining word embedding and ASCII transcoding techniques. The model can recognize all sorts of SQL injection attacks more efficiently and accurately, dramatically lower human intervention, and offer some defense against 0day attacks that never occur.

关键词： SQL injection ASCII transcoding 1d convolutional neural network Attack Recognition

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Cloud detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1d convolutional neural network

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REMOTE SENSING 2021年第16期13卷 3319页

作者： Ma, Nan Sun, Lin Zhou, Chenghu He, Yawen China Univ Petr Sch Geosci Qingdao 266580 Peoples R China Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Chinese Acad Sci Inst Geog Sci & Nat Resources Res State Key Lab Resources & Environm Informat Syst Beijing 100101 Peoples R China China Univ Petr Coll Oceanog & Space Informat Qingdao 266580 Peoples R China

Automatic cloud detection in remote sensing images is of great significance. deep-learning-based methods can achieve cloud detection with high accuracy;however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (Cd-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1d-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MOdIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MOdIS cloud mask, support vector machine, and random forest. The comparison revealed that the Cd-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI

关键词： ASTER spectral library hyperspectral data 1d convolutional neural network cloud detection data simulation multi-satellite remote sensing images

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Preterm Birth Prediction by Classification of Spectral Features of Electrohysterography Signals using 1d convolutional neural network: Preliminary Results 28

Preterm Birth Prediction by Classification of Spectral Featu...

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28th Signal Processing and Communications Applications Conference (SIU)

作者： degbedzui, derek Kweku Yuksel, Mehmet Emin Malik, Afrah Elfatih Farah Erciyes Univ Fac Engn Dept Biomed Engn TR-38039 Kayseri Turkey

ISBN: (纸本)9781728172064

The assessment of uterine contraction provides valuable information about the progress of labour. deliveries occurring before the estimated date introduce undesirable consequences for the fetus and mother. Electrohysterography (EHG), a noninvasive monitoring method, is the most preferred and studied method to diagnose preterm delivery and minimize its unpleasant consequences. This paper proposes a novel feature extraction method for non-invasive diagnosis of preterm delivery using EHG signals. The acquired EHG signals are sliced into frames for spectral analysis. Next, the centroid frequencies of the frames are computed to establish a feature vector. Using a 1d convolutional neural network (CNN), the effectiveness of the feature vector is compared with existing methods in the literature. The experimental results confirm that the proposed method shows superior performance and can be used to predict term-preterm birth more accurately.

关键词： Term-preterm prediction Electrohysterography Spectral analysis 1d convolutional neural network

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ASM1d-GAN: An Intelligent Fault diagnosis Method Based on Assembled 1d convolutional neural network and Generative Adversarial networks

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE ANd VIdEO TECHNOLOGY 2019年第10期91卷 1237-1247页

作者： Gao, Shengyao Wang, Xueren Miao, Xuhong Su, Changwei Li, Yibin China Naval Acad Beijing 100161 Peoples R China Shandong Univ IMST Qingdao 266520 Shandong Peoples R China

For the past few years, In the research of intelligent monitoring of industrial equipment, deep learning is becoming a method that get the widespread concern of researchers. In general, the collection of a great quantity of typical data that has been labeled makes the deep learning approach a great success. However, it is often limited by real fault data samples, and the generalization ability of the established model is poor. A novel fault diagnostic method that we assemble data generation and fault diagnosis called ASM1d-GAN is proposed to address these problems. This method is composed of 1d convolutional neural network, Generative Adversarial networks (GANs), and fault classifier. We assemble the data generation and fault diagnosis procedures together. Through a new antagonistic machine learning mechanism, the ASM1d-GAN model is optimized, so as to achieve higher sample quality and fault mode classification ability. This novel method can extract fault features from natural fault samples and generate effective new ones. The experimental results demonstrate the effective fault feature generation ability and the superior fault diagnostic ability of the proposed method.

关键词： Generative Adversarial networks 1d convolutional neural network Fault diagnosis Artificial intelligence

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Applying singular value decomposition on accelerometer data for 1d convolutional neural network based fall detection

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ELECTRONICS LETTERS 2019年第6期55卷 320-+页

作者： Cho, H. Yoon, S. M. Kookmin Univ Seoul South Korea

The usefulness of applying singular value decomposition (SVd) on triaxial accelerometer data for one-dimensional (1d) convolutional neural network (CNN) based fall and activity recognition is investigated. Three-dimensional reduction methods, namely, SVd, sparse principal component analysis, and kernel principal component analysis, are compared for their effectiveness in extracting useful features for fall and activity recognition. Experiments conducted on three public falls and activities of daily living datasets show that SVd applied to acceleration data coupled with raw acceleration data or acceleration signal magnitude vector exhibited better 1d CNN fall and activity recognition accuracy than those using other principal component analysis based acceleration features.

关键词： accelerometers principal component analysis singular value decomposition feature extraction feedforward neural nets activity recognition principal component analysis based acceleration features singular value decomposition 1d convolutional neural network fall detection SVd triaxial accelerometer data one-dimensional convolutional neural network based fall three-dimensional reduction methods sparse principal component analysis kernel principal component analysis useful features public falls raw acceleration data CNN

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