Improving performance of heart rate time series classification by grouping subjects

• URL: http://arxiv.org/abs/2311.13285v1
• Date: Wed, 22 Nov 2023 10:08:33 GMT
• Title: Improving performance of heart rate time series classification by grouping subjects
• Authors: Michael Beekhuizen (1), Arman Naseri (1 and 2), David Tax (1), Ivo van der Bilt (2), Marcel Reinders (1) ((1) Delft University of Technology, (2) Haga Teaching Hospital)
• Abstract summary: The accuracy is sensitive to the choice of window/stride size. Various techniques were used to minimize subject variability. Heart rate time series can be utilized for classification tasks like predicting activity.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unlike the more commonly analyzed ECG or PPG data for activity classification, heart rate time series data is less detailed, often noisier and can contain missing data points. Using the BigIdeasLab_STEP dataset, which includes heart rate time series annotated with specific tasks performed by individuals, we sought to determine if general classification was achievable. Our analyses showed that the accuracy is sensitive to the choice of window/stride size. Moreover, we found variable classification performances between subjects due to differences in the physical structure of their hearts. Various techniques were used to minimize this variability. First of all, normalization proved to be a crucial step and significantly improved the performance. Secondly, grouping subjects and performing classification inside a group helped to improve performance and decrease inter-subject variability. Finally, we show that including handcrafted features as input to a deep learning (DL) network improves the classification performance further. Together, these findings indicate that heart rate time series can be utilized for classification tasks like predicting activity. However, normalization or grouping techniques need to be chosen carefully to minimize the issue of subject variability.

Related papers

• Revisit Time Series Classification Benchmark: The Impact of Temporal Information for Classification [7.331937231993605]
  We perform permutation tests that disrupt temporal information on the UCR time series classification archive. We identify a significant proportion of datasets where temporal information has little to no impact on classification. We propose UCR Augmented, a benchmark based on the UCR time series classification archive designed to evaluate classifiers' ability to extract and utilize temporal information.
  arXiv  Detail & Related papers  (2025-03-26T06:13:41Z)
• VSFormer: Value and Shape-Aware Transformer with Prior-Enhanced Self-Attention for Multivariate Time Series Classification [47.92529531621406]
  We propose a novel method, VSFormer, that incorporates both discriminative patterns (shape) and numerical information (value) In addition, we extract class-specific prior information derived from supervised information to enrich the positional encoding. Extensive experiments on all 30 UEA archived datasets demonstrate the superior performance of our method compared to SOTA models.
  arXiv  Detail & Related papers  (2024-12-21T07:31:22Z)
• Understanding the Detrimental Class-level Effects of Data Augmentation [63.1733767714073]
  achieving optimal average accuracy comes at the cost of significantly hurting individual class accuracy by as much as 20% on ImageNet. We present a framework for understanding how DA interacts with class-level learning dynamics. We show that simple class-conditional augmentation strategies improve performance on the negatively affected classes.
  arXiv  Detail & Related papers  (2023-12-07T18:37:43Z)
• Balanced Classification: A Unified Framework for Long-Tailed Object Detection [74.94216414011326]
  Conventional detectors suffer from performance degradation when dealing with long-tailed data due to a classification bias towards the majority head categories. We introduce a unified framework called BAlanced CLassification (BACL), which enables adaptive rectification of inequalities caused by disparities in category distribution. BACL consistently achieves performance improvements across various datasets with different backbones and architectures.
  arXiv  Detail & Related papers  (2023-08-04T09:11:07Z)
• Robust Explainer Recommendation for Time Series Classification [4.817429789586127]
  Time series classification is a task common in domains such as human activity recognition, sports analytics and general sensing. Recently, a great variety of techniques have been proposed and adapted for time series to provide explanation in the form of saliency maps. This paper provides a novel framework to quantitatively evaluate and rank explanation methods for time series classification.
  arXiv  Detail & Related papers  (2023-06-08T18:49:23Z)
• Time Series Contrastive Learning with Information-Aware Augmentations [57.45139904366001]
  A key component of contrastive learning is to select appropriate augmentations imposing some priors to construct feasible positive samples. How to find the desired augmentations of time series data that are meaningful for given contrastive learning tasks and datasets remains an open question. We propose a new contrastive learning approach with information-aware augmentations, InfoTS, that adaptively selects optimal augmentations for time series representation learning.
  arXiv  Detail & Related papers  (2023-03-21T15:02:50Z)
• Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network [9.227037203895533]
  Exercise testing has been available for more than a half-century and is a versatile tool for diagnostic and prognostic information of patients for a range of diseases. In this work, we encode the time series as images using the Gramian Angular Field and Markov Transition Field. We use it with a convolutional neural network and attention pooling approach for the classification of heart failure and metabolic syndrome patients.
  arXiv  Detail & Related papers  (2022-04-26T16:49:06Z)
• Self-supervised Pretraining with Classification Labels for Temporal Activity Detection [54.366236719520565]
  Temporal Activity Detection aims to predict activity classes per frame. Due to the expensive frame-level annotations required for detection, the scale of detection datasets is limited. This work proposes a novel self-supervised pretraining method for detection leveraging classification labels.
  arXiv  Detail & Related papers  (2021-11-26T18:59:28Z)
• Learnable Dynamic Temporal Pooling for Time Series Classification [22.931314501371805]
  We present a dynamic temporal pooling (DTP) technique that reduces the temporal size of hidden representations by aggregating the features at the segment-level. For the partition of a whole series into multiple segments, we utilize dynamic time warping (DTW) to align each time point in a temporal order with the prototypical features of the segments. The DTP layer combined with a fully-connected layer helps to extract further discriminative features considering their temporal position within an input time series.
  arXiv  Detail & Related papers  (2021-04-02T08:58:44Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)
• Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View [82.80085730891126]
  We provide the first modernally precise analysis of linear multiclass classification. Our analysis reveals that the classification accuracy is highly distribution-dependent. The insights gained may pave the way for a precise understanding of other classification algorithms.
  arXiv  Detail & Related papers  (2020-11-16T05:17:29Z)
• Complexity Measures and Features for Times Series classification [0.0]
  We propose a set of characteristics capable of extracting information on the structure of the time series to face time series classification problems. The experimental results of our proposal show no statistically significant differences from the second and third best models of the state-of-the-art.
  arXiv  Detail & Related papers  (2020-02-27T11:08:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.