Online anomaly detection in ECG signal using Hierarchical Temporal Memory

Midani, Wissal; Fki, Zeineb; BenAyed, Mounir

doi:10.1109/icabme47164.2019.8940307

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…This architecture enhances the model's ability to identify rare anomalies in complex medical temporal data. For instance, Wissal et al [52] applied HTM to ECG signals, achieving exceptional anomaly detection in heart arrhythmias. Experimental results demonstrated that, compared to deep learning, HTM is more effective at detecting anomalies in ECG signals.…”

Section: G Hierarchical Temporal Memory (Htm)mentioning

confidence: 99%

Deep Learning Technologies for Time Series Anomaly Detection in Healthcare: A Review

Yang,

Qi,

Zhou

2023

IEEE Access

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Medical time series data often exhibit intricate and dynamic patterns. With the rapid advancement of medical digitization, deep learning-based time series anomaly detection techniques have found extensive applications in the healthcare field, such as detecting irregular heart rhythms and monitoring patients' vital signs. By reviewing and summarizing the relevant research, this paper explores the deep learning-based time series anomaly detection techniques within the medical and health domain, analyzing the strengths and limitations of different deep learning architectures and algorithms in tackling specific medical tasks. Lastly, we discuss the challenges faced by this field and outline future research directions.

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Section: G Hierarchical Temporal Memory (Htm)mentioning

confidence: 99%

Deep Learning Technologies for Time Series Anomaly Detection in Healthcare: A Review

Yang,

Qi,

Zhou

2023

IEEE Access

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“…In recent years, some scholars start to implement TPL on hardware [27]. Many scholars combine SPL with TPL and apply HTM to many fields with time series data, such as anomaly detection of time series data [28], heart attack detection [29], medical data flow prediction [25], hydrological intelligent monitoring [30], abnormal ECG detection [31], and abnormal detection in crowd management [32]. However, when the sequence data is learned as rules, TPL in the above research still has some limitations.…”

Section: Htm Is a New Artificial Neural Network Model Based On Jeffmentioning

confidence: 99%

A Temporal Pool Learning Algorithm Based on Location Awareness

Zhu

Cai

et al. 2021

Scientific Programming

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Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms.

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