Deep Learning Convolutional Neural Network for ECG Signal Classification Aggregated Using IoT

Recently, the development of the Internet of Things (IoT) has enabled continuous and personal electrocardiogram (ECG) monitoring. In the ECG monitoring system, classification plays an important role because it can select useful data (i.e., reduce the size of the dataset) and identify abnormal data that can be used to detect the clinical diagnosis and guide further treatment. Since the classification requires computing capability, the ECG data are usually delivered to the gateway or the server where the classification is performed based on its computing resource. However, real-time ECG data transmission continuously consumes battery and network resources, which are expensive and limited. To mitigate this problem, this paper proposes a tiny machine learning (TinyML)-based classification (i.e., TinyCES), where the ECG monitoring device performs the classification by itself based on the machine-learning model, which can reduce the memory and the network resource usages for the classification. To demonstrate the feasibility, after we configure the convolutional neural networks (CNN)-based model using ECG data from the Massachusetts Institute of Technology (MIT)-Beth Israel Hospital (BIH) arrhythmia and the Physikalisch Technische Bundesanstalt (PTB) diagnostic ECG databases, TinyCES is validated using the TinyML-supported Arduino prototype. The performance results show that TinyCES can have an approximately 97% detection ratio, which means that it has great potential to be a lightweight and resource-efficient ECG monitoring system.

In the current article, an automatic classification of cardiac arrhythmias is presented using a transfer deep learning approach with the help of electrocardiography (ECG) signal analysis. Now a days, an ECG waveform serves as a powerful tool used for the analysis of cardiac arrhythmias (irregularities). The goal of the present work is to implement an algorithm based on deep learning for classification of different cardiac arrhythmias. Initially, the one dimensional (1-D) ECG signals are transformed to two dimensional (2-D) scalogram images with the help of Continuous Wavelet(CWT). Four different categories of ECG waveform were selected from four PhysioNet MIT-BIH databases, namely arrhythmia database, Normal Sinus Rhythm database, Malignant Ventricular Ectopy database and BIDMC Congestive heart failure database to examine the proposed technique. The major interest of the present study is to develop a transferred deep learning algorithm for automatic categorization of the mentioned four different heart diseases. Final results proved that the 2-D scalogram images trained with a deep convolutional neural network CNN with transfer learning technique (AlexNet) pepped up with a prominent accuracy of 95.67%. Hence, it is worthwhile to say the above stated algorithm demonstrates as an effective automated heart disease detection tool

PurposeThe aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs.Materials and MethodsIn order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords (deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed.ResultsWhen dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects.ConclusionClinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.

Introduction Catathrenia is a rare sleep-related breathing disorder characterized by recurrent monotonous groaning during sleep. The acoustic characteristics of groaning and snoring sounds have been investigated. This study aims to propose a deep convolutional neural network (CNN) for automatic binary classification. Methods This study consisted of 3728 episodes of groaning sounds and 4577 episodes of snoring sounds obtained from synchronized audio of full-night polysomnography. Four features extracted from log-scaled mel-spectrograms were used as input. The background gaussian noise and the time-shifting were used to augment the dataset. The dataset was randomly split into training (70%), validation (15%), and testing datasets (15%). A deep learning convolutional neural network architecture was trained and evaluated. Results The proposed CNN model achieves an accuracy of 95.0% on the binary classification of groaning and snoring sounds. The model attains a sensitivity/recall of 96.4%, a specificity of 93.4%, and an F1 score of 94.84%. Conclusion The proposed CNN architecture has performed well in the automatic binary classification of groaning and snoring sounds, which could reduce difficulties in acoustic analyses of groaning episode detection. The model needs to be verified with more audio data before it can be put into clinical use better. Support (if any)

Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learning convolutional neural networks (DL-CNNs) have demonstrated dermatologist-level accuracy in identifying CM. We hypothesized that support from DL-CNN could increase dermatologists' accuracy in differentiating CM from MelSim. This cross-sectional reader study analyzed the diagnostic performance of a DL-CNN and 27 dermatologists for 200 skin lesions (100 CM, 100 MelSim). The DL-CNN assigned malignancy scores ranging from 0 to 1 (> 0.5 indicating malignancy). Dermatologists assessed lesions across three levels: (I) dermoscopy only, (II) full case information (dermoscopy, close-up images, metadata), and (III) full case information plus DL-CNN scores. Primary outcomes were sensitivity, specificity, and ROC-AUC of dermatologists with or without DL-CNN-support (level-II versus -III). The DL-CNN and dermatologists in level-II showed a comparable sensitivity (95 % CI) of 90.0 % (82.6-94.5 %) and 90.1 % (86.9-93.2 %, p = 0.153), respectively. However, the DL-CNN's specificity (67.0 % (57.3-75.4 %) versus 73.2 % (69.1-77.3 %)) and ROC-AUC (0.889 (0.845-0.932) versus 0.951 (0.920-0.982)) were significantly lower than for dermatologists (all p < 0.01). When dermatologists integrated DL-CNN predictions (level-III) their sensitivity increased to 91.4 % (88.3-94.5 %, p < 0.001) without markedly changing specificity (74.2 % (70.6-77.7 %, p = 0.435)) or ROC-AUC (0.954 (0.927-0.982, p = 0.581)). Collaboration with a DL-CNN slightly improved dermatologists' diagnostic accuracy in a mixed CM and MelSim dataset, by increasing sensitivity without a loss of specificity. The DL-CNN's level of sensitivity in this difficult-to-diagnose dataset underlines the potential as an assistant diagnostic tool.

Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil‐based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence‐based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI‐SHES with Internet of Things (IoT) environment. The proposed AI‐SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real‐time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android‐based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI‐SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI‐SHES with IoT disclose superior performance in terms of accuracy, F‐measure with 99.29%, and 99.23%, respectively.

Accurate short-term traffic flow forecasting facilitates active traffic control and trip planning. Most existing traffic flow models fail to make full use of the temporal and spatial features of traffic data. This study proposes a short-term traffic flow prediction model based on a convolution neural network (CNN) deep learning framework. In the proposed framework, the optimal input data time lags and amounts of spatial data are determined by a spatio-temporal feature selection algorithm (STFSA), and selected spatio-temporal traffic flow features are extracted from actual data and converted into a two-dimensional matrix. The CNN then learns these features to construct a predictive model. The effectiveness of the proposed method is evaluated by comparing the forecast results with actual traffic data. Other existing models are also evaluated for comparison. The proposed method outperforms baseline models in terms of accuracy.

World Health Organization (WHO) reported that heart disease, stroke, lower respiratory infections and chronic obstructive lung disease have remained the top major killers in the world during the past decade. In Indonesia, it covers 37% or about 1.8 millions mortality cases. Hence, human vital sign monitoring such as electrocardiograph (ECG) monitoring system is very essential for mitigating and preventing people hearth's fail to be handled a proper assistance from healthcare physicians. For that purpose, we propose a real time ECG monitoring system, which the ECG data is monitored wirelessly directly without any server for data pooling (centralized database). ECG QRS wave and arrhythmia detection are important features for ECG monitoring system. Up to now, there are several studies ECG wave detection, however, they are mainly focus on internet of things - based telemedicine system for data transfer. In this case, real time monitoring system could be disrupted by a pooling time and packet loss due to its IP-based connection. Hence, we propose a wireless ECG patient monitoring system based on point to point communication scheme at 2.4 GHz band by using 3-lead acquisition system on single channel wireless ECG device. The system is developed based on AD8232 chip for acquisition, NRF24101 wireless link platform for data transfer and Arduino™microcontroller for ECG data processing. In order to evaluate the performance of wireless ECG system, our developed wearable textile antennas (as Tx and Rx antenna) are attached on the human body and experimentally evaluated within indoor environment, simulating off-/on-body communication system. The wearable textile antenna is lightweight, small and insensitive to the resonant frequency due to the human body effect. The measurement is conducted by embedding the antennas at some body positions of the volunteers. The experimental results show that the proposed ECG monitoring system is able to transfer and display real time ECG signals well for off-/on-body communication schemes. In addition, to validate the results, the obtained ECG data are compared and analyzed with the healthcare clinic equipment. According to QRS wave analysis, the ECG signals have reasonable RR, PR, QRS and ST wave interval in comparison with the standard ECG signals. We convince that our developed ECG patient monitoring system is stable by applying our developed textile antennas and it is promising to be adopted in the hospital for future implementation.

Background: The healthcare sector is experiencing a transformative shift due to advancements in technology, particularly with the Internet of Things (IoT). IoT integration in healthcare is poised to revolutionize patient monitoring and management, particularly for individuals with chronic conditions. The Grand View Research Inc. analysis projected a significant increase in IoT penetration in healthcare, with a market value of approximately $409.9 billion by 2022. Aim: This article aims to explore the applications, benefits, and future potential of IoT devices in real-time health monitoring for patients with chronic conditions. Methods: The review encompasses various IoT-based health monitoring systems, including wearable and implantable devices, biosensors, and remote patient monitoring systems. The methodologies of existing IoT applications, such as the UbiMon project and various ZigBee-based systems, are analyzed to understand their impact on patient care. Results: IoT technologies facilitate real-time monitoring of vital signs, improve chronic disease management, and enhance emergency response systems. Examples include smart inhalers, ECG monitors, and remote surgery systems. The integration of IoT in healthcare has led to improved patient outcomes, reduced emergency waiting times, and better resource management in hospitals. Conclusion: IoT is transforming healthcare by enabling continuous, real-time monitoring of chronic conditions and enhancing overall patient care.

This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.

The study was aimed to explore the effect of information health data based on deep learning of neural network to diagnose the infectious factors of patients with chronic glomerular disease (CGD) and evaluate its diagnostic effect. Ninety patients with CGD were selected and randomly rolled into control group A, control group B, and observation group, with 30 cases in each group. Big data scientific research analysis platform was used for data integration, convolutional neural network (CNN) was employed for feature analysis, correlation analysis, and screening of disease‐related biomarkers. The patients were diagnosed by observation of symptoms and signs, combined diagnosis of blood test and urine test, and information health data diagnosis based on deep learning CNN. As a result, the specificity, sensitivity, and accuracy of information health data diagnosis based on deep learning CNN were 78.9%, 87.6%, and 92.1%, respectively. The main sources of infections in patients were lung infections, bloodstream infections, urinary system infections, skin and soft tissue infections, and upper respiratory tract infections. Amongst them, lung infection accounted for the highest proportion, reaching 65.4%, followed by blood infection (11.2%) and skin tissue infection (9.6%). The pathogens of infection were mainly bacteria, viruses, fungi, tuberculosis, and pneumocystis pneumonia (PCP), amongst which bacterial infections accounted for the highest proportion (31.5%), followed by PCP (25.6%). In short, the information health data based on deep learning CNN had high specificity, sensitivity, and accuracy for the diagnosis of CGD. The main infectious factors of CGD were pulmonary infection and blood infection, and the pathogens were mainly bacteria and viruses.

The rapid development in the wearable sensors helped develop the healthcare-based Internet of Things (IoT) platforms for continuous monitoring of chronic disease. As per the World Health Organization (WHO) 85% of the Cardiovascular Disease (CVD) is preventable with proper monitoring for symptoms and awareness. The electrocardiogram (ECG) signal refers to a recording of electrical activity that is generated by the human heart. An analysis of the ECG signal helps in building models that can make an automatic classification of various CVDs. Deep neural network is found to be efficient to make an automatic classification of the primary signals of the ECG. These ECG signals to the arrhythmia classes were grouped by using the Convolutional Neural Networks (CNN). The 9 Layers of the CNN techniques are employed in state-of-the-art modern research to classify the signal. The Tabu Search (TS) algorithm with the CNN, Hybrid Optimization Algorithm known as the CNN with ABC-TS, and CNN with Artificial Bee Colony (ABC) is used to optimize the CNN in this work for classifying ECG signals. An evaluation of the technique was made on the basis of metrics of performance evaluation that are capable of producing enhanced outcomes.

A comparison of various machine learning algorithms was carried out, including a multilayer perceptron in combination with principal component analysis and linear discriminant analysis, as well as deep learning convolutional neural networks combined with wavelet analysis for the classification of electrocorticographic (ECoG) data to identify the effects of experimental anticonvulsants. The study was performed on 36 non-linear male rats weighing 250–290 g. For electrocorticogram (ECoG) recording, cortical electrodes were implanted in the animals, and after substance administration, signals were recorded (30 minutes) with a sampling frequency of 500 Hz and electrode impedance 5 kOhm. The rats were divided into three groups: group 1 (n = 8) was given intraperitoneal saline solution 1 ml/kg; group 2 (n = 8) was injected with carbamazepine 25 mg/kg; group 3 (n = 6) was injected with compound RU-1205 (20 mg/kg). The obtained signals were segmented into 5-second epochs. For each channel, 15 features were extracted and processed by principal component analysis and linear discriminant analysis. Alternatively, the Morlet wavelet transform was used to obtain spectrograms. Classification was carried out using a multilayer perceptron and a convolutional neural network. The constructed models were evaluated on electrocorticographic signals from a test set of 2160 samples, which were recorded after administration of compound RU-1205. The accuracy of the models ranged from 71–84 %. Moreover, the accuracy scores for all three models were statistically significantly different from the random classifier (p 0,05). The study showed that artificial neural networks, including multilayer perceptron and convolutional neural networks, can be effectively applied to classify electrocorticographic signals and determine the anticonvulsant activity of various compounds.

Smart agriculture with IoT (Internet of things) gives rise to agribusiness and may fulfill the demand of food in the world. Agribusiness will become more productive when IoT is introduced. As the farmers are becoming more aware to IoT, the possibility of introducing new technologies is gaining momentum in agribusiness. The IoT is highly efficient, accessible, persistent and of exposed nature. The farmers may use sensors for monitoring the crops, soil and also analyze the crop production. This will give boost up to precision agriculture. The information about farms like temperature, moisture level and soil mineral level, pH value of soil and air quality can be collected remotely by using IoT on smartphones or computer systems. Today’s IoT techniques and devices are so advanced and are mainly application dependent providing smart systems to be deployed in specific areas. In this paper, a review of IoT devices and protocols is presented and it also throws light on issues arise during the implementation of IoT in agriculture.

Auto-detection and segmentation of involved lymph nodes in HPV-associated oropharyngeal cancer using a convolutional deep learning neural network