Qrs detection based on medical knowledge and cascades of moving average filters

Heartbeat detection is the first step in automatic analysis of the electrocardiogram (ECG). For mobile and wearable devices, the detection process should be both accurate and computationally efficient. In this paper, we present a QRS detection algorithm based on moving average filters, which affords a simple yet robust signal processing technique. The decision logic considers the rhythmic and morphological features of the QRS complex. QRS enhancing is performed with channel-specific moving average cascades selected from a pool of derivative systems we designed. We measured the effectiveness of our algorithm on well-known benchmark databases, reporting F1 scores, sensitivity on abnormal beats and processing time. We also evaluated the performances of other available detectors for a direct comparison with the same criteria. The algorithm we propose achieved satisfying performances on par with or higher than the other QRS detectors. Despite the performances we report are not the highest that have been published so far, our approach to QRS detection enhances computational efficiency while maintaining high accuracy

Computer Aided ECG Analysis - State of the Art and Upcoming Challenges

In this paper we present current achievements in computer aided ECG analysis and their applicability in real world medical diagnosis process. Most of the current work is covering problems of removing noise, detecting heartbeats and rhythm-based analysis. There are some advancements in particular ECG segments detection and beat classifications but with limited evaluations and without clinical approvals. This paper presents state of the art advancements in those areas till present day. Besides this short computer science and signal processing literature review, paper covers future challenges regarding the ECG signal morphology analysis deriving from the medical literature review. Paper is concluded with identified gaps in current advancements and testing, upcoming challenges for future research and a bullseye test is suggested for morphology analysis evaluation.Comment: 7 pages, 3 figures, IEEE EUROCON 2013 International conference on computer as a tool, 1-4 July 2013, Zagreb, Croati

Sensors for Vital Signs Monitoring

Sensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data

A lightweight QRS detector for single lead ECG signals using a max-min difference algorithm

Background and objectives - Detection of the R-peak pertaining to the QRS complex of an ECG signal plays an important role for the diagnosis of a patient's heart condition. To accurately identify the QRS locations from the acquired raw ECG signals, we need to handle a number of challenges, which include noise, baseline wander, varying peak amplitudes, and signal abnormality. This research aims to address these challenges by developing an efficient lightweight algorithm for QRS (i.e., R-peak) detection from raw ECG signals. Methods - A lightweight real-time sliding window-based Max-Min Difference (MMD) algorithm for QRS detection from Lead II ECG signals is proposed. Targeting to achieve the best trade-off between computational efficiency and detection accuracy, the proposed algorithm consists of five key steps for QRS detection, namely, baseline correction, MMD curve generation, dynamic threshold computation, R-peak detection, and error correction. Five annotated databases from Physionet are used for evaluating the proposed algorithm in R-peak detection. Integrated with a feature extraction technique and a neural network classifier, the proposed ORS detection algorithm has also been extended to undertake normal and abnormal heartbeat detection from ECG signals. Results - The proposed algorithm exhibits a high degree of robustness in QRS detection and achieves an average sensitivity of 99.62% and an average positive predictivity of 99.67%. Its performance compares favorably with those from the existing state-of-the-art models reported in the literature. In regards to normal and abnormal heartbeat detection, the proposed QRS detection algorithm in combination with the feature extraction technique and neural network classifier achieves an overall accuracy rate of 93.44% based on an empirical evaluation using the MIT-BIH Arrhythmia data set with 10-fold cross validation. Conclusions - In comparison with other related studies, the proposed algorithm offers a lightweight adaptive alternative for R-peak detection with good computational efficiency. The empirical results indicate that it not only yields a high accuracy rate in QRS detection, but also exhibits efficient computational complexity at the order of O(n), where n is the length of an ECG signal

A HARDWARE-SOFTWARE CO-DESIGNED WEARABLE FOR REAL-TIME PHYSIOLOGICAL DATA COLLECTION AND SIGNAL QUALITY ASSESSMENT

In the future, Smart and Connected Communities (S&CC) will use distributed wireless sensors and embedded computing platforms to produce meaningful data that can help individuals, and communities. Here, we presented a scanner, a data reliability estimation algorithm and Electrocardiogram (ECG) beat classification algorithm which contributes to the S&CC framework .In part 1, we report the design, prototyping, and functional validation of a low-power, small, and portable signal acquisition device for these sensors. The scanner was fully tested, characterized, and validated in the lab, as well as through deployment to users homes. As a test case, we show results of the scanner measuring WRAP temperature sensors with relative error within the 0.01% range. The scanner measurement shows distinguish temperature of 1F difference and excellent linear dependence between actual and measured resistance (R2 = 0.998). This device hasdemonstrated the possibility of a small, low-power portable scanner for WRAP sensors.Additionally, we explored the statistical data reliability metric (DReM) to explain the quality of bio-signal quantitatively on a scale between 0.0 -1.0. As proof of concept, we analyzed the ECG signal. Our DReM prediction algorithm measures the reliability of the ECG signals effectively with low Root mean square error = 0.010 and Mean absolute error = 0.008 and coefficient of determination R2 value of 0.990. Finally, we tested our model against the opinions of three independent judges and presented R2 value to determine the agreement between judgments vs our prediction model.We concluded our contribution to the S&CC framework by analyzing ECG beat classification with a pipeline of classifiers that focuses on improving the models performance on identifying minority classes (ventricular ectopic beat, supraventricular ectopic beat). Moreover, we intended to minimize morphological distortion introduced due to indiscriminate use of filtering techniques on ECG signals. Our approach shows an average positive predictive value 95.21%, sensitivity of95.28%, and F-1 score 95.76% respectively

Development of a Real-Time Single-Lead Single-Beat Frequency-Independent Myocardial Infarction Detector

The central aim of this research is the development and deployment of a novel multilayer machine learning design with unique application for the diagnosis of myocardial infarctions (MIs) from individual heartbeats of single-lead electrocardiograms (EKGs) irrespective of their sampling frequencies over a given range. To the best of our knowledge, this design is the first to attempt inter-patient myocardial infarction detection from individual heartbeats of single-lead (lead II) electrocardiograms that achieves high accuracy and near real-time diagnosis. The processing time of 300 milliseconds to a diagnosis is just at the time range in between extremely fast heartbeats of around 300 milliseconds, or 200 beats per minute. The design achieves stable performance metrics over the frequency range of 202Hz to 2.8kHz with an accuracy of 77.12%, positive predictive value (PPV) of 75.85%, and a negative predictive value (NPV) of 83.02% over the entire PTB database; 85.07%, 81.54%, 87.31% over the PTB-XL (the largest EKG database available for research) validation set, and 84.17%, 78.37%, 87.55% over the PTB-XL test set. Major design contributions and findings of this work reveal (1) a method for the realtime detection of ventricular depolarization events in the PQRST complex from 12-lead electrocardiograms using Independent Component Analysis (ICA), with a slightly different use of ICA proposed for electrocardiogram analysis and R-peak detection/localization; (2) a multilayer Long-Short Term Memory (LSTM) neural network design that identifies infarcted patients from a single heartbeat of a single-lead (lead II) electrocardiogram; (3) and integrated LSTM neural network with an algorithm that detects the R-peaks in real time for instantaneous detection of myocardial infarctions and for effective monitoring of patients under cardiac stress and/or at risk of myocardial infarction; (4) a fully integrated 12-lead real-time classifier with even higher detection metrics and a deeper neural architecture, which could serve as a near real-time monitoring tool that could gauge disease progression and evaluate benefits gained from early intervention and treatment planning; (5) a real-time frequency-independent design based on a single-lead single-beat MI detector, which is of pivotal importance to deployment as there is no standard sampling frequency for EKGs, making them span a wider frequency spectrum. vi

Time-Delay Switch Attack on Networked Control Systems, Effects and Countermeasures

In recent years, the security of networked control systems (NCSs) has been an important challenge for many researchers. Although the security schemes for networked control systems have advanced in the past several years, there have been many acknowledged cyber attacks. As a result, this dissertation proposes the use of a novel time-delay switch (TDS) attack by introducing time delays into the dynamics of NCSs. Such an attack has devastating effects on NCSs if prevention techniques and countermeasures are not considered in the design of these systems. To overcome the stability issue caused by TDS attacks, this dissertation proposes a new detector to track TDS attacks in real time. This method relies on an estimator that will estimate and track time delays introduced by a hacker. Once a detector obtains the maximum tolerable time delay of a plant’s optimal controller (for which the plant remains secure and stable), it issues an alarm signal and directs the system to its alarm state. In the alarm state, the plant operates under the control of an emergency controller that can be local or networked to the plant and remains in this stable mode until the networked control system state is restored. In another effort, this dissertation evaluates different control methods to find out which one is more stable when under a TDS attack than others. Also, a novel, simple and effective controller is proposed to thwart TDS attacks on the sensing loop (SL). The modified controller controls the system under a TDS attack. Also, the time-delay estimator will track time delays introduced by a hacker using a modified model reference-based control with an indirect supervisor and a modified least mean square (LMS) minimization technique. Furthermore, here, the demonstration proves that the cryptographic solutions are ineffective in the recovery from TDS attacks. A cryptography-free TDS recovery (CF-TDSR) communication protocol enhancement is introduced to leverage the adaptive channel redundancy techniques, along with a novel state estimator to detect and assist in the recovery of the destabilizing effects of TDS attacks. The conclusion shows how the CF-TDSR ensures the control stability of linear time invariant systems