scholarly journals Wearable Real-Time Heart Attack Detection and Warning System to Reduce Road Accidents

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2780 ◽  
Author(s):  
Muhammad E. H. Chowdhury ◽  
Khawla Alzoubi ◽  
Amith Khandakar ◽  
Ridab Khallifa ◽  
Rayaan Abouhasera ◽  
...  
Keyword(s):  
Real Time ◽  
Heart Attack ◽  
The United States ◽  
Attack Detection ◽  
The Other ◽  
Support Vector ◽  
Linear Classification ◽  
Road Accidents ◽  
Time Frequency ◽  
St Elevation

Heart attack is one of the leading causes of human death worldwide. Every year, about 610,000 people die of heart attack in the United States alone—that is one in every four deaths—but there are well understood early symptoms of heart attack that could be used to greatly help in saving many lives and minimizing damages by detecting and reporting at an early stage. On the other hand, every year, about 2.35 million people get injured or disabled from road accidents. Unexpectedly, many of these fatal accidents happen due to the heart attack of drivers that leads to the loss of control of the vehicle. The current work proposes the development of a wearable system for real-time detection and warning of heart attacks in drivers, which could be enormously helpful in reducing road accidents. The system consists of two subsystems that communicate wirelessly using Bluetooth technology, namely, a wearable sensor subsystem and an intelligent heart attack detection and warning subsystem. The sensor subsystem records the electrical activity of the heart from the chest area to produce electrocardiogram (ECG) trace and send that to the other portable decision-making subsystem where the symptoms of heart attack are detected. We evaluated the performance of dry electrodes and different electrode configurations and measured overall power consumption of the system. Linear classification and several machine algorithms were trained and tested for real-time application. It was observed that the linear classification algorithm was not able to detect heart attack in noisy data, whereas the support vector machine (SVM) algorithm with polynomial kernel with extended time–frequency features using extended modified B-distribution (EMBD) showed highest accuracy and was able to detect 97.4% and 96.3% of ST-elevation myocardial infarction (STEMI) and non-ST-elevation MI (NSTEMI), respectively. The proposed system can therefore help in reducing the loss of lives from the growing number of road accidents all over the world.

Cardiovascular Applications of Artificial Intelligence in Research, Diagnosis, and Disease Management

2022 ◽  
pp. 80-127
Author(s):  
Viswanathan Rajagopalan ◽  
Houwei Cao
Keyword(s):  
Artificial Intelligence ◽  
Disease Management ◽  
Heart Attack ◽  
Congenital Heart ◽  
The United States ◽  
Disease Classification ◽  
Gradient Boosting ◽  
Support Vector ◽  
Gradient Boosting Machine ◽  
Infarction Heart

Despite significant advancements in diagnosis and disease management, cardiovascular (CV) disorders remain the No. 1 killer both in the United States and across the world, and innovative and transformative technologies such as artificial intelligence (AI) are increasingly employed in CV medicine. In this chapter, the authors introduce different AI and machine learning (ML) tools including support vector machine (SVM), gradient boosting machine (GBM), and deep learning models (DL), and their applicability to advance CV diagnosis and disease classification, and risk prediction and patient management. The applications include, but are not limited to, electrocardiogram, imaging, genomics, and drug research in different CV pathologies such as myocardial infarction (heart attack), heart failure, congenital heart disease, arrhythmias, valvular abnormalities, etc.

scholarly journals Design a Monitoring Device for Heart-Attack Early Detection Based on Respiration Rate and Body Temperature Parameters

2021 ◽  
Vol 3 (3) ◽  
pp. 114-120
Author(s):  
Isna Fatimatuz Zahra ◽  
I Dewa Gede Hari Wisana ◽  
Priyambada Cahya Nugraha ◽  
Hayder J Hassaballah
Keyword(s):  
Body Temperature ◽  
Real Time ◽  
Respiration Rate ◽  
Heart Attack ◽  
Detection System ◽  
Warning System ◽  
Piezoelectric Sensor ◽  
Attack Detection ◽  
Sensor Data ◽  
Frequency Module

Acute myocardial infarction, commonly referred to as a heart attack, is the most common cause of sudden death where a monitoring tool is needed that is equipped with a system that can notify doctors to take immediate action. The purpose of this study was to design a heart attack detection device through indicators of vital human signs. The contribution of this research is that the system works in real-time, has more parameters, uses wireless, and is equipped with a system to detect indications of a heart attack. In order for wireless monitoring to be carried out in real-time and supported by a detection system, this design uses a radio frequency module as data transmission and uses a warning system that is used for detection. Respiration rate was measured using the piezoelectric sensor, and body temperature was measured using the DS18B20 temperature sensor. Processing of sensor data is done with ESP32, which is displayed wirelessly by the HC-12 module on the PC. If an indication of a heart attack is detected in the parameter value, the tool will activate a notification on the PC. In every indication of a heart attack, it was found that this design can provide notification properly. The results showed that the largest respiratory error value was 4%, and the largest body temperature error value was 0.55%. The results of this study can be implemented in patients who have been diagnosed with heart attack disease so that it can facilitate monitoring the patient's condition.

scholarly journals The Heart Attack Detection by ESP8266 Data Communication at a Real Time to Avoid Sudden Death

2019 ◽  
Vol 1235 ◽  
pp. 012044
Author(s):  
Poltak Sihombing ◽  
Mangasa Manullang ◽  
Dahlan Sitompul ◽  
Imelda Sri Dumayanti
Keyword(s):  
Sudden Death ◽  
Real Time ◽  
Heart Attack ◽  
Data Communication ◽  
Attack Detection

scholarly journals Near-Real-Time IDS for the U.S. FAA’s NextGen ADS-B

2021 ◽  
Vol 5 (2) ◽  
pp. 27
Author(s):  
Dustin M. Mink ◽  
Jeffrey McDonald ◽  
Sikha Bagui ◽  
William B. Glisson ◽  
Jordan Shropshire ◽  
...  
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Detection System ◽  
Attack Detection ◽  
Support Vector ◽  
Network Attack ◽  
Ground Station ◽  
Traffic Systems ◽  
Attack Mitigation ◽  
The U.S

Modern-day aircraft are flying computer networks, vulnerable to ground station flooding, ghost aircraft injection or flooding, aircraft disappearance, virtual trajectory modifications or false alarm attacks, and aircraft spoofing. This work lays out a data mining process, in the context of big data, to determine flight patterns, including patterns for possible attacks, in the U.S. National Air Space (NAS). Flights outside the flight patterns are possible attacks. For this study, OpenSky was used as the data source of Automatic Dependent Surveillance-Broadcast (ADS-B) messages, NiFi was used for data management, Elasticsearch was used as the log analyzer, Kibana was used to visualize the data for feature selection, and Support Vector Machine (SVM) was used for classification. This research provides a solution for attack mitigation by packaging a machine learning algorithm, SVM, into an intrusion detection system and calculating the feasibility of processing US ADS-B messages in near real time. Results of this work show that ADS-B network attacks can be detected using network attack signatures, and volume and velocity calculations show that ADS-B messages are processable at the scale of the U.S. Next Generation (NextGen) Air Traffic Systems using commodity hardware, facilitating real time attack detection. Precision and recall close to 80% were obtained using SVM.

Real-time control of a prosthetic hand using human electrocorticography signals

2011 ◽  
Vol 114 (6) ◽  
pp. 1715-1722 ◽  
Author(s):  
Takufumi Yanagisawa ◽  
Masayuki Hirata ◽  
Youichi Saitoh ◽  
Tetsu Goto ◽  
Haruhiko Kishima ◽  
...  
Keyword(s):  
Real Time ◽  
Hand Movement ◽  
Hand Movements ◽  
Support Vector ◽  
Prosthetic Hand ◽  
Real Time Control ◽  
Calibration Period ◽  
Frequency Bands ◽  
Time Frequency ◽  
Movement Type

Object A brain-machine interface (BMI) offers patients with severe motor disabilities greater independence by controlling external devices such as prosthetic arms. Among the available signal sources for the BMI, electrocorticography (ECoG) provides a clinically feasible signal with long-term stability and low clinical risk. Although ECoG signals have been used to infer arm movements, no study has examined its use to control a prosthetic arm in real time. The authors present an integrated BMI system for the control of a prosthetic hand using ECoG signals in a patient who had suffered a stroke. This system used the power modulations of the ECoG signal that are characteristic during movements of the patient's hand and enabled control of the prosthetic hand with movements that mimicked the patient's hand movements. Methods A poststroke patient with subdural electrodes placed over his sensorimotor cortex performed 3 types of simple hand movements following a sound cue (calibration period). Time-frequency analysis was performed with the ECoG signals to select 3 frequency bands (1–8, 25–40, and 80–150 Hz) that revealed characteristic power modulation during the movements. Using these selected features, 2 classifiers (decoders) were trained to predict the movement state—that is, whether the patient was moving his hand or not—and the movement type based on a linear support vector machine. The decoding accuracy was compared among the 3 frequency bands to identify the most informative features. With the trained decoders, novel ECoG signals were decoded online while the patient performed the same task without cues (free-run period). According to the results of the real-time decoding, the prosthetic hand mimicked the patient's hand movements. Results Offline cross-validation analysis of the ECoG data measured during the calibration period revealed that the state and movement type of the patient's hand were predicted with an accuracy of 79.6% (chance 50%) and 68.3% (chance 33.3%), respectively. Using the trained decoders, the onset of the hand movement was detected within 0.37 ± 0.29 seconds of the actual movement. At the detected onset timing, the type of movement was inferred with an accuracy of 69.2%. In the free-run period, the patient's hand movements were faithfully mimicked by the prosthetic hand in real time. Conclusions The present integrated BMI system successfully decoded the hand movements of a poststroke patient and controlled a prosthetic hand in real time. This success paves the way for the restoration of the patient's motor function using a prosthetic arm controlled by a BMI using ECoG signals.

scholarly journals Machine Learning in Wearable Biomedical Systems

2020 ◽  
Author(s):  
Muhammad E.H. Chowdhury ◽  
Amith Khandakar ◽  
Yazan Qiblawey ◽  
Mamun Bin Ibne Reaz ◽  
Mohammad Tariqul Islam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Diabetic Foot ◽  
Heart Attack ◽  
Blood Pressure Monitoring ◽  
Algorithm Design ◽  
Health Condition ◽  
Attack Detection ◽  
Signal Acquisition ◽  
Biomedical Systems

Wearable technology has added a whole new dimension in the healthcare system by real-time continuous monitoring of human body physiology. They are used in daily activities and fitness monitoring and have even penetrated in monitoring the health condition of patients suffering from chronic illnesses. There are a lot of research and development activities being pursued to develop more innovative and reliable wearable. This chapter will cover discussions on the design and implementation of wearable devices for different applications such as real-time detection of heart attack, abnormal heart sound, blood pressure monitoring, gait analysis for diabetic foot monitoring. This chapter will also cover how the signals acquired from these prototypes can be used for training machine learning (ML) algorithm to diagnose the condition of the person wearing the device. This chapter discusses the steps involved in (i) hardware design including sensors selection, characterization, signal acquisition, and communication to decision-making subsystem and (ii) the ML algorithm design including feature extraction, feature reduction, training, and testing. This chapter will use the case study of the design of smart insole for diabetic foot monitoring, wearable real-time heart attack detection, and smart-digital stethoscope system to show the steps involved in the development of wearable biomedical systems.

scholarly journals Classification of Complex Emotions Using EEG and Virtual Environment: Proof of Concept and Therapeutic Implication

2021 ◽  
Vol 15 ◽  
Author(s):  
Eleonora De Filippi ◽  
Mara Wolter ◽  
Bruno R. P. Melo ◽  
Carlos J. Tierra-Criollo ◽  
Tiago Bortolini ◽  
...  
Keyword(s):  
Real Time ◽  
Virtual Environment ◽  
Cross Validation ◽  
Support Vector ◽  
Neurofeedback Training ◽  
Time Frequency ◽  
Alpha Asymmetry ◽  
Complex Emotions ◽  
Eeg Neurofeedback

During the last decades, neurofeedback training for emotional self-regulation has received significant attention from scientific and clinical communities. Most studies have investigated emotions using functional magnetic resonance imaging (fMRI), including the real-time application in neurofeedback training. However, the electroencephalogram (EEG) is a more suitable tool for therapeutic application. Our study aims at establishing a method to classify discrete complex emotions (e.g., tenderness and anguish) elicited through a near-immersive scenario that can be later used for EEG-neurofeedback. EEG-based affective computing studies have mainly focused on emotion classification based on dimensions, commonly using passive elicitation through single-modality stimuli. Here, we integrated both passive and active elicitation methods. We recorded electrophysiological data during emotion-evoking trials, combining emotional self-induction with a multimodal virtual environment. We extracted correlational and time-frequency features, including frontal-alpha asymmetry (FAA), using Complex Morlet Wavelet convolution. Thinking about future real-time applications, we performed within-subject classification using 1-s windows as samples and we applied trial-specific cross-validation. We opted for a traditional machine-learning classifier with low computational complexity and sufficient validation in online settings, the Support Vector Machine. Results of individual-based cross-validation using the whole feature sets showed considerable between-subject variability. The individual accuracies ranged from 59.2 to 92.9% using time-frequency/FAA and 62.4 to 92.4% using correlational features. We found that features of the temporal, occipital, and left-frontal channels were the most discriminative between the two emotions. Our results show that the suggested pipeline is suitable for individual-based classification of discrete emotions, paving the way for future personalized EEG-neurofeedback training.

Working With Interpreters to Support Students Who Are English Language Learners

2016 ◽  
Vol 1 (16) ◽  
pp. 15-27 ◽  
Author(s):  
Henriette W. Langdon ◽  
Terry Irvine Saenz
Keyword(s):  
United States ◽  
English Language Learners ◽  
Language Learners ◽  
English Language ◽  
First Language ◽  
The United States ◽  
The Other ◽  
Primary Language ◽  
Second Best ◽  
Support Students

The number of English Language Learners (ELL) is increasing in all regions of the United States. Although the majority (71%) speak Spanish as their first language, the other 29% may speak one of as many as 100 or more different languages. In spite of an increasing number of speech-language pathologists (SLPs) who can provide bilingual services, the likelihood of a match between a given student's primary language and an SLP's is rather minimal. The second best option is to work with a trained language interpreter in the student's language. However, very frequently, this interpreter may be bilingual but not trained to do the job.

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