scholarly journals Robust and Accurate Anomaly Detection in ECG Artifacts Using Time Series Motif Discovery

2015 ◽  
Vol 2015 ◽  
pp. 1-20 ◽  
Author(s):  
Haemwaan Sivaraks ◽  
Chotirat Ann Ratanamahatana
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Motif Discovery ◽  
Expert Knowledge ◽  
Academic Research ◽  
Detection Methods ◽  
Ecg Signals ◽  
Sensitivity Specificity ◽  
Ecg Artifacts

Electrocardiogram (ECG) anomaly detection is an important technique for detecting dissimilar heartbeats which helps identify abnormal ECGs before the diagnosis process. Currently available ECG anomaly detection methods, ranging from academic research to commercial ECG machines, still suffer from a high false alarm rate because these methods are not able to differentiate ECG artifacts from real ECG signal, especially, in ECG artifacts that are similar to ECG signals in terms of shape and/or frequency. The problem leads to high vigilance for physicians and misinterpretation risk for nonspecialists. Therefore, this work proposes a novel anomaly detection technique that is highly robust and accurate in the presence of ECG artifacts which can effectively reduce the false alarm rate. Expert knowledge from cardiologists and motif discovery technique is utilized in our design. In addition, every step of the algorithm conforms to the interpretation of cardiologists. Our method can be utilized to both single-lead ECGs and multilead ECGs. Our experiment results on real ECG datasets are interpreted and evaluated by cardiologists. Our proposed algorithm can mostly achieve 100% of accuracy on detection (AoD), sensitivity, specificity, and positive predictive value with 0% false alarm rate. The results demonstrate that our proposed method is highly accurate and robust to artifacts, compared with competitive anomaly detection methods.

An improved real-valued negative selection algorithm based on the constant detector for anomaly detection

2021 ◽  
Vol 40 (5) ◽  
pp. 8793-8806
Author(s):  
Dong Li ◽  
Xin Sun ◽  
Furong Gao ◽  
Shulin Liu
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
State Space ◽  
False Alarm Rate ◽  
Negative Selection ◽  
Detection Rate ◽  
Detection Methods ◽  
Selection Algorithm ◽  
Negative Selection Algorithm ◽  
Synthetic Datasets

Compared with the traditional negative selection algorithms produce detectors randomly in whole state space, the boundary-fixed negative selection algorithm (FB-NSA) non-randomly produces a layer of detectors closely surrounding the self space. However, the false alarm rate of FB-NSA is higher than many anomaly detection methods. Its detection rate is very low when normal data close to the boundary of state space. This paper proposed an improved FB-NSA (IFB-NSA) to solve these problems. IFB-NSA enlarges the state space and adds auxiliary detectors in appropriate places to improve the detection rate, and uses variable-sized training samples to reduce the false alarm rate. We present experiments on synthetic datasets and the UCI Iris dataset to demonstrate the effectiveness of this approach. The results show that IFB-NSA outperforms FB-NSA and the other anomaly detection methods in most of the cases.

scholarly journals Hyperspectral Anomaly Detection via Spatial Density Background Purification

2019 ◽  
Vol 11 (22) ◽  
pp. 2618 ◽  
Author(s):  
Bing Tu ◽  
Nanying Li ◽  
Zhuolang Liao ◽  
Xianfeng Ou ◽  
Guoyun Zhang
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Local Density ◽  
Detection Performance ◽  
Detection Methods ◽  
Collaborative Representation ◽  
Spatial Density ◽  
Small Window ◽  
Local Densities

In the research of anomaly detection methods, obtaining a pure background without abnormal pixels can effectively improve the detection performance and reduce the false-alarm rate. Therefore, this paper proposes a spatial density background purification (SDBP) method for hyperspectral anomaly detection. First, a density peak clustering (DP) algorithm is used to calculate the local density of pixels within a single window. Then, the local densities are sorted into descending order and the m pixels that have the highest local density are selected from high to low. Therefore, the potential abnormal pixels in the background can be effectively removed, and a purer background set can be obtained. Finally, the collaborative representation detector (CRD) is employed for anomaly detection. Considering that the neighboring area of each pixel will have homogeneous material pixels, we adopt the double window strategy to improve the above method. The local densities of the pixels between the large window and the small window are calculated, while all pixels are removed from the small window. This makes the background estimation more accurate, reduces the false-alarm rate, and improves the detection performance. Experimental results on three real hyperspectral datasets such as Airport, Beach, and Urban scenes indicate that the detection accuracy of this method outperforms other commonly used anomaly detection methods.

A Novel Network Traffic Anomaly Detection Based on Multi-Scale Fusion

2011 ◽  
Vol 48-49 ◽  
pp. 102-105
Author(s):  
Guo Zhen Cheng ◽  
Dong Nian Cheng ◽  
He Lei
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Network Traffic ◽  
Self Similarity ◽  
Data Set ◽  
Multi Scale ◽  
Traffic Anomaly ◽  
Detection Evaluation ◽  
Better Than

Detecting network traffic anomaly is very important for network security. But it has high false alarm rate, low detect rate and that can’t perform real-time detection in the backbone very well due to its nonlinearity, nonstationarity and self-similarity. Therefore we propose a novel detection method—EMD-DS, and prove that it can reduce mean error rate of anomaly detection efficiently after EMD. On the KDD CUP 1999 intrusion detection evaluation data set, this detector detects 85.1% attacks at low false alarm rate which is better than some other systems.

scholarly journals Adjacent Infrared Multitarget Detection Using Robust Background Estimation

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Sungho Kim ◽  
Kyung-Tae Kim
Keyword(s):  
False Alarm ◽  
False Alarm Rate ◽  
Target Detection ◽  
Detection Method ◽  
Detection Performance ◽  
Detection Methods ◽  
Constant False Alarm Rate ◽  
Cluttered Environments ◽  
Background Estimation ◽  
Cfar Detection

Small target detection is very important for infrared search and track (IRST) problems. Grouped targets are difficult to detect using the conventional constant false alarm rate (CFAR) detection method. In this study, a novel multitarget detection method was developed to identify adjacent or closely spaced small infrared targets. The neighboring targets decrease the signal-to-clutter ratio in hysteresis threshold-based constant false alarm rate (H-CFAR) detection, which leads to poor detection performance in cluttered environments. The proposed adjacent target rejection-based robust background estimation can reduce the effects of the neighboring targets and enhance the small multitarget detection performance in infrared images by increasing the signal-to-clutter ratio. The experimental results of the synthetic and real adjacent target sequences showed that the proposed method produces an upgraded detection rate with the same false alarm rate compared to the recent target detection methods (H-CFAR, Top-hat, and TDLMS).

scholarly journals MINIMAL LEARNING MACHINE IN ANOMALY DETECTION FROM HYPERSPECTRAL IMAGES

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 467-472
Author(s):  
I. Pölönen ◽  
K. Riihiaho ◽  
A.-M. Hakola ◽  
L. Annala
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Data Gathering ◽  
Classification Algorithm ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Computationally Efficient ◽  
Learning Machine ◽  
Minimal Learning Machine

Abstract. Anomaly detection from hyperspectral data needs computationally efficient methods to process the data when the data gathering platform is a drone or a cube satellite. In this study, we introduce a minimal learning machine for hyperspectral anomaly detection. Minimal learning machine is a novel distance-based classification algorithm, which is now modified to detect anomalies. Besides being computationally efficient, minimal learning machine is also easy to implement. Based on the results, we show that minimal learning machine is efficient in detecting global anomalies from the hyperspectral data with low false alarm rate.

scholarly journals Change Detection Based on the Coefficient of Variation in SAR Time-Series of Urban Areas

2020 ◽  
Vol 12 (13) ◽  
pp. 2089 ◽  
Author(s):  
Elise Colin Koeniguer ◽  
Jean-Marie Nicolas
Keyword(s):  
Time Series ◽  
Change Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Coefficient Of Variation ◽  
Urban Areas ◽  
Real Data ◽  
Probability Of Detection ◽  
Detection Methods ◽  
Coefficients Of Variations

This paper discusses change detection in SAR time-series. First, several statistical properties of the coefficient of variation highlight its pertinence for change detection. Subsequently, several criteria are proposed. The coefficient of variation is suggested to detect any kind of change. Furthermore, several criteria that are based on ratios of coefficients of variations are proposed to detect long events, such as construction test sites, or point-event, such as vehicles. These detection methods are first evaluated on theoretical statistical simulations to determine the scenarios where they can deliver the best results. The simulations demonstrate the greater sensitivity of the coefficient of variation to speckle mixtures, as in the case of agricultural plots. Conversely, they also demonstrate the greater specificity of the other criteria for the cases addressed: very short event or longer-term changes. Subsequently, detection performance is assessed on real data for different types of scenes and sensors (Sentinel-1, UAVSAR). In particular, a quantitative evaluation is performed with a comparison of our solutions with baseline methods. The proposed criteria achieve the best performance, with reduced computational complexity. On Sentinel-1 images containing mainly construction test sites, our best criterion reaches a probability of change detection of 90% for a false alarm rate that is equal to 5%. On UAVSAR images containing boats, the criteria proposed for short events achieve a probability of detection equal to 90% of all pixels belonging to the boats, for a false alarm rate that is equal to 2%.

scholarly journals Interface Detector Based on Vaccination Strategy for Anomaly Detection

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Yinghui Liu ◽  
Dong Li ◽  
Yuan Wei ◽  
Hongli Zhang
Keyword(s):  
Anomaly Detection ◽  
False Alarm ◽  
False Alarm Rate ◽  
Adaptive Learning ◽  
Detection Rate ◽  
Vaccination Strategy ◽  
Negative Selection Algorithm ◽  
Training Samples ◽  
Testing Stage ◽  
And Training

Interface detector is an enhanced negative selection algorithm with online adaptive learning under small training samples for anomaly detection. It has better detection performance when it has an appropriate self-radius. Otherwise, overfitting or underfitting would occur. In the present paper, an improved interface detector, which is based on vaccination strategy, is proposed. During the testing stage, negative vaccine can overcome overfitting to improve the detection rate and positive vaccine can overcome underfitting to reduce the false alarm rate. The experimental results show that under the same dataset, self-radius, and training samples condition, the detection rate of the interface detector with negative vaccine is much higher than that of interface detector, SVM, and BP neural network. Moreover, the false alarm rate of the interface detector with positive vaccine is much lower than that of the interface detector and PSA.

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