Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects (original) (raw)
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Cardiovascular diseases (CVDs) are the number one cause of death globally. An estimated 17.9 million people die from CVDs each year, representing 31% of all global deaths. Most cardiac patients require early detection and treatment. Therefore, many products to monitor patient’s heart conditions have been introduced on the market. Most of these devices can record a patient’s bio-metric signals both in resting and in exercising situations. However, reading the massive amount of raw electrocardiogram (ECG) signals from the sensors is very time-consuming. Automatic anomaly detection for the ECG signals could act as an assistant for doctors to diagnose a cardiac condition. This paper reviews the current state-of-the-art of this technology discusses the pros and cons of the devices and algorithms found in the literature and the possible research directions to develop the next generation of ambulatory monitoring systems.
Anomaly Detection and Classification of Physiological Signals in IoT- Based Healthcare Framework
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Physiological signals retrieve information from sensors implanted or attached to the human body. These signals are vital data sources that can assist in predicting the disease well before time; thus, proper treatment can be made possible. With the addition of the Internet of Things in healthcare, real-time data collection and preprocessing for signal analysis has reduced the burden of in-person appointments and decision making on healthcare. Recently, deep learning-based algorithms have been implemented by researchers for the recognition, realization and prediction of diseases by extracting and analyzing important features. In this research, real-time 1-D time series data of on-body noninvasive biomedical sensors were acquired, preprocessed and analysed for anomaly detection. Feature engineered parameters of large and diverse datasets have been used to train the data to make the anomaly detection system more reliable. For comprehensive real-time monitoring, the implemented system us...
Online anomaly detection for long-term ECG monitoring using wearable devices
Pattern Recognition, 2019
Many successful algorithms for analyzing ECG signals leverage data-driven models that are learned for each specific user. Unfortunately, a few algorithmic challenges are still to be addressed before employing these models in wearable devices, thus enabling online and long-term monitoring. In particular, since the heartbeats morphology changes with the heart rate, models learned in resting conditions need to be adapted to analyze ECG signals recorded during everyday activities. We propose an online ECG monitoring solution where normal heartbeats of each specific user are modeled by dictionaries yielding sparse representations, and heartbeats that do not conform to this model are detected as anomalous. We track heart rate variations by adapting the user-specific dictionary with a set of user-independent, linear, transformations. Our experiments demonstrate that these transformations can be successfully learned from a public dataset of ECG signals and that, thanks to an optimized anomaly-detection algorithm, our solution enables online and long-term ECG monitoring.
Sensors, 2013
The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems.
Wearable Sensors in Health Monitoring Systems
Recent years have perceived an increase in the progress of wearable sensors for health monitoring systems. This increase has been due to several issues such as development in sensor technology as well as focused efforts on political and investor levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. In this system is about study of how the data is treated and processed. This paper provides latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital symbols in healthcare services. This paper outlines the data mining tasks that have been applied such as prediction, anomaly detection and decision making when considering in particular continuous time series measurements and detailed about the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental support.
ECGGAN: A Framework for Effective and Interpretable Electrocardiogram Anomaly Detection
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Heart is the most important organ of the human body, and Electrocardiogram (ECG) is an essential tool for clinical monitoring of heart health and detecting cardiovascular diseases. Automatic detection of ECG anomalies is of great significance and clinical value in healthcare. However, performing automatic anomaly detection for the ECG data is challenging because we not only need to accurately detect the anomalies but also need to provide clinically meaningful interpretation of the results. Existing works on automatic ECG anomaly detection either rely on hand-crafted designs of feature extraction algorithms which are typically too simple to deliver good performance, or deep learning for automatically extracting features, which is not interpretable. In this paper, we propose ECGGAN, a novel reconstruction-based ECG anomaly detection framework. The key idea of ECGGAN is to make full use of the characteristics of ECG with the periodic metadata, namely beat, to learn the universal pattern in ECG from representative normal data. We establish a reconstruction model, taking leads as constraints to capture the unique characteristics of different leads in ECG data, and achieve accurate anomaly detection at ECG-level by combining multiple leads. Experimental results on two real-world datasets and their mixed-set confirm that our method achieves superior performance than baselines in terms of precision, recall, F1-score, and AUC. In addition, ECGGAN can provide clinically meaningful interpretation of results by revealing the extent to which abnormal sites deviate from the normal pattern. CCS CONCEPTS • Applied computing → Health care information systems; • Human-centered computing → Scientific visualization.
Outlier detection for patient monitoring and alerting
Journal of Biomedical Informatics, 2013
We develop and evaluate a data-driven approach for detecting unusual (anomalous) patient-management actions using past patient cases stored in an electronic health record (EHR) system. Our hypothesis is that patient-management actions that are unusual with respect to past patients may be due to a potential error and that it is worthwhile to raise an alert if such a condition is encountered. We evaluate this hypothesis using data obtained from the electronic health records of 4,486 post-cardiac surgical patients. We base the evaluation on the opinions of a panel of experts. The results support that anomaly-based alerting can have reasonably low false alert rates and that stronger anomalies are correlated with higher alert rates.
Interpretable Rule Mining for Real-Time ECG Anomaly Detection in IoT Edge Sensors
IEEE Internet of Things Journal
Electrocardiogram (ECG) analysis is widely used in the diagnosis of cardiovascular diseases. This article proposes an explainable rule-mining strategy for prioritizing abnormal class detection in ECG data. The proposed method utilizes a biased-trained artificial neural network (ANN) with input features derived from an ECG beat sequence and formulates a set of rules at each node of an on-demand tree-like search algorithm. The rule base at each node is derived from a linear combination of the most impactful features identified using gradient analysis in an ANN. The final derived model is an explainable rule-based system that detects abnormal heartbeats based on statistical and morphological features from ECG. The model achieves the target sensitivity, and accuracy with a low run time complexity through a comprehensive offline rule-mining process and is trained using the MIT-BIH Arrhythmia Database. The system achieves an accuracy of 93% with only nine nodes and a test sensitivity of 90% and 80%, respectively, for VEB and SVEB beat types, when tested on previously unseen ECG data from the INCART database. The model performance and complexity can be easily adjusted based on the real-time resource constraints of a wearable sensor. The model was deployed on an ARM Cortex M4-based embedded device and is shown to achieve a > 50% reduction in sensor power consumption when only abnormal beats are wirelessly transmitted. That is, RF transmission is gated using the model output and transmission is disabled when the subject's ECG is normal. The proposed technique is highly suited for healthcare applications because of its explainability, lower complexity, and real-time flexibility when deployed in the Internet of Things (IoT)-enabled wearable edge sensors. Index Terms-Anomaly detection, artificial neural network (ANN), electrocardiogram (ECG), explainability, heart rate variability (HRV), Internet of Things (IoT) edge sensors, rule-based algorithm. I. INTRODUCTION A RRHYTHMIAS, such as respiratory sinus arrhythmia, are a natural periodic variation in the heart rhythm and typically do not have negative consequences to the individual's
Anomaly Detection in Clinical Data of Patients Undergoing Heart Surgery
Procedia Computer Science, 2017
We describe two approaches to detecting anomalies in time series of multi-parameter clinical data: (1) metric and model-based indicators and (2) information surprise. (1) Metric and model-based indicators are commonly used as early warning signals to detect transitions between alternate states based on individual time series. Here we explore the applicability of existing indicators to distinguish critical (anomalies) from non-critical conditions in patients undergoing cardiac surgery, based on a small anonymized clinical trial dataset. We find that a combination of time-varying autoregressive model, kurtosis, and skewness indicators correctly distinguished critical from non-critical patients in 5 out of 36 blood parameters at a window size of 0.3 (average of 37 hours) or higher. (2) Information surprise quantifies how the progression of one patient's condition differs from that of rest of the population based on the cross-section of time series. With the maximum surprise and slope features we detect all critical patients at the 0.05 significance level. Moreover we show that a naive outlier detection does not work, demonstrating the need for the more sophisticated approaches explored here. Our preliminary results suggest that future developments in early warning systems for patient condition monitoring may predict the onset of critical transition and allow medical intervention preventing patient death. Further method development is needed to avoid overfitting and spurious results, and verification on large clinical datasets.
Online mining abnormal period patterns from multiple medical sensor data streams
World Wide Web, 2013
With the advanced technology of medical devices and sensors, an abundance of medical data streams are available. However, data analysis techniques are very limited, especially for processing massive multiple physiological streams that may only be understood by medical experts. The state-of-the-art techniques only allow multiple medical devices to independently monitor different physiological parameters for the patient's status, thus they signal too many false alarms, creating unnecessary noise, especially in the Intensive Care Unit (ICU). An effective solution which has been recently studied is to integrate information from multiple physiologic parameters to reduce alarms. But it is a challenge to detect abnormalities from high frequently changed physiological streams data, since abnormalities occur gradually due to the complex situation of patients. An analysis of ICU physiological data streams shows that many vital physiological parameters are changed periodically (such as heart rate, arterial pressure, and respiratory impedance) and thus abnormalities are generally abnormal period patterns. In this paper, we develop a Mining Abnormal Period Patterns from Multiple Physiological Streams (MAPPMPS) method to detect and rank abnormalities in medical sensor streams. The efficiency and effectiveness of the MAPPMPS method is demonstrated by a real-world massive database of multiple physiological streams sampled in ICU, comprising 250 patients' streams (each stream involving over 1.3 million data points) with a total size of 28 GB data.