The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other ‘things’ ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers’ heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries.

/pdf/an-iot-based-computational-framework-for-healthcare-480zcfp6qs.pdf

An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

/pdf/automatic-qrs-complex-detection-using-two-level-2op06tzma8.pdf

Automatic QRS complex detection using two-level convolutional neural network

ECG signal abnormality detection is useful for identifying heart related problems. Two popular abnormality detection techniques are ischaemic beat classification and arrhythmic beat classification. In this work, ECG signal preprocessing and KNN based arrhythmic beat classification are performed to categorize into normal and abnormal subjects. LMS based adaptive filters are used in ECG signal preprocessing, but they consume more time for processing due to long critical path. To overcome this problem, a novel adaptive filter with delayed error normalized LMS algorithm is utilized to attain high speed and low latency design. Low power design is achieved in this design by applying pipelining concept in the error feedback path. R-peak detection is carried out in the preprocessed signal using wavelets for HRV feature extraction. Arrhythmic beat classification is carried out by KNN classifier on HRV feature extracted signal. Classification performance reveals that the proposed DWT with KNN classifier provides the accuracy of 97.5% which is better than other machine leaning techniques.

A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection

Cardiac-related diseases have been one major cause of death for an ever increasing number of patients over the last few decades throughout the world. In response, automatic classification of cardiac rhythms using Heart Rate Variability analysis as an effective diagnostic tool has recently emerged as an important field of research. Previous researches has proved that translating and transforming HRV data into numbers can introduce highly accurate assessments of rhythm disorders. However, to obtain reliable HRV interpretation, accurate QRS detection approaches must be utilized. This work, as motivated by the arguments just presented, reviews in detail the most recent and efficient techniques related to QRS feature extraction and HRV determination all classified and presented in a convenient fashion to facilitate coverage. The study also presents a state-of-the-art updated review on QRS detection and heart rate variability analyses that could serve as a handy future reference in this field of research based on more than 200 articles reviewed in this effort.

/pdf/qrs-detection-and-heart-rate-variability-analysis-a-survey-17tpeb28w3.pdf

QRS Detection and Heart Rate Variability Analysis: A Survey

Coronary angiography image segmentation based on PSPNet.

New-Onset Atrial Fibrillation in Patients Presenting With Acute Myocardial Infarction: Predictors and Impact on In-Hospital Outcomes and Mortality

In Tunisian agriculture, olive tree cultivation plays an important role. It is affected by different stresses that jeopardize its sustainability. In this context, our objective is to enhance the resilience of this crop. To achieve this goal, our work consists of detecting anomalies at early stage starting from the tree to the field scale. The proposed solution takes advantage of the emergence of satellites with high spatial and temporal resolution. In particular, the Sentinel-2 sensor which is well-adapted to monitor the vegetation. It is characterized by ten spectral bands allowing to access to key vegetation properties such as leaf area index (LAI), chlorophyll content (Cab) and water content (Cw), etc. Direct estimation of these parameters for the image is not practical as the signal is convolved. For that, we use artificial intelligence techniques to separate the effects of the different properties. We develop an Artificial Neural Network (ANN) that learn to estimate the vegetation properties given the pixel signature. The learning is done using a database of simulated data produced by a radiative transfer model that simulates the satellite image given the vegetation cover properties. The stress detection is based on a threshold on tree LAI and Cab. Comparison with ground truth with healthy and stressed plots has shown the validity of our approach.

Olive Tree Health Monitoring Approach Using Satellite Images and Based on Artificial Intelligence: Toward Automatic Olive Stress Detection Solution

Covid-19 is a highly contagious respiratory syndrome, officially declared a global pandemic on 11 March 2020. Due to its rapid spread and the exponential increase in the number of infected and deceased patients, manual diagnosis in the healthcare sector is insufficient to manage each patient individually, even the assessment of lesions by clinicians is approximate. Moreover, to date, no end-to-end tool is proposed for automatic volumetric quantification of Covid lesions. Hence, in this paper we report the implementation of a complete chain for automatic assessment of the degree of Covid-19 lesions. It includes (i) preparation of the private database, (ii) image pre-processing, (iii) automatic segmentation based on U-NET and evaluation of its results by the usual metrics, (iv) 3D reconstruction and finally (v) volumetric quantification of Covid-19 lesions using the digitised images as input. For validation, the process is applied to our own private database that we have created for this purpose. The results obtained are very encouraging. The evaluation of the segmentation for the lung by the metrics DICE, IOU, Precision, Recall and Accuracy yielded respectively: 0.81, 0.90, 0.93, 0.82 and 0.92. Similarly for lesions these values are: 0.89, 0.93, 0.93, 0.81 and 0.93 respectively.

Asma Ben Abdallah

Papers

New-Onset Atrial Fibrillation in Patients Presenting With Acute Myocardial Infarction: Predictors and Impact on In-Hospital Outcomes and Mortality

Olive Tree Health Monitoring Approach Using Satellite Images and Based on Artificial Intelligence: Toward Automatic Olive Stress Detection Solution

Automatic evaluation of 3D volume infection rates: Application to the Covid-19 context

Fast Unsupervised Residual Attention GAN for COVID-19 Detection

Convolutional Neural Network Approach for Multiple Sclerosis Lesion Segmentation