Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing

Lü, Ping; Ghiasi, Shadi; Hagenah, J.; Hai, Ho Bich; Hảo, Nguyễn Văn; Khanh, Phan Nguyen Quoc; Le, Dan; Thwaites, Louise; Clifton, David A.; Zhu, Tingting

doi:10.3390/s22176554

Cited by 5 publications

(28 citation statements)

References 41 publications

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“…The diagnosis of lethal infectious diseases plays a significant role in patient treatment. In tetanus, disease severity is associated with autonomic nervous system (ANS) activity [13]. Heart rate variability (HRV) represents the variation of beat-to-beat in RR intervals.…”

Section: Related Workmentioning

confidence: 99%

“…Heart rate variability (HRV) represents the variation of beat-to-beat in RR intervals. This variation is controlled by the autonomic nervous system (ANS) and it indicates ANS activity [13]. The changes in conventional HRV parameters measured from ECG have been shown to correlate with tetanus disease severity.…”

Section: Related Workmentioning

confidence: 99%

“…We resize and stitch on the 2D logarithmic spectrogram. In our work, we choose 60 second ECGs based on previous experiments in Lu et al [13] [18].…”

Section: B Time Series Imagingmentioning

confidence: 99%

“…These convolutional blocks extract rich local spatial features in each 2D spectrogram. The architecture of each block was inspired by Lu et al [13] and Zihlmann et al [23]. Each convolutional block contains the 2D convolutional layers (3 × 3 kernel size), exponential linear unit (ELU) and 2D batch normalization.…”

Section: Cnn-transformer Networkmentioning

confidence: 99%

“…In this paper, we use ECG data collected with wearable sensors in a Vietnamese ICU and propose a warning tool, created using a deep learning approach, in order to classify tetanus severity indicated by Ablett score. In our recent study [13], we used 2D Convolutional Neural Network (CNN) with a channel-wise attention mechanism to classify the severity of tetanus using wearable monitors in a resource-limited setting. Our method outperforms 1D CNN, 2D CNN and 2D CNN with attention mechanism by combining either the gating function or sequential techniques.…”

Section: Introductionmentioning

confidence: 99%

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Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Lü

Wang

Hagenah

et al. 2023

IEEE Trans. Biomed. Eng.

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Tetanus is a life-threatening infectious disease, which is still common in low-and middle-income countries, including in Vietnam. This disease is characterized by muscle spasm and in severe cases is complicated by autonomic dysfunction. Ideally continuous vital sign monitoring using bedside monitors allows the prompt detection of the onset of autonomic nervous system dysfunction or avoiding rapid deterioration. Detection can be improved using heart rate variability analysis from ECG signals. Recently, characteristic ECG and heart rate variability features have been shown to be of value in classifying tetanus severity. However, conventional manual analysis of ECG is time-consuming. The traditional convolutional neural network (CNN) has limitations in extracting the global context information, due to its fixed-sized kernel filters. In this work, we propose a novel hybrid CNN-Transformer model to automatically classify tetanus severity using tetanus monitoring from low-cost wearable sensors. This model can capture the local features from the CNN and the global features from the Transformer. The time series imaging -spectrogram -is transformed from one-dimensional ECG signal and input to the proposed model. The CNN-Transformer model outperforms state-of-the-art methods in tetanus classification, achieves results with a F1 score of 0.82 ± 0.03, precision of 0.94 ± 0.03, recall of 0.73 ± 0.07, specificity of 0.97 ± 0.02, accuracy of 0.88 ± 0.01 and AUC of 0.85 ± 0.03. In addition, we found that Random Forest with enough manually selected features can be comparable with the proposed CNN-Transformer model.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…We resize and stitch on the 2D logarithmic spectrogram. In our work, we choose 60 second ECGs based on previous experiments in Lu et al [13] [18].…”

Section: B Time Series Imagingmentioning

confidence: 99%

Section: Cnn-transformer Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Lü

Wang

Hagenah

et al. 2023

IEEE Trans. Biomed. Eng.

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Feasibility of wearable monitors to detect heart rate variability in children with hand, foot and mouth disease

Nhan,

Hung,

Khanh

et al. 2024

BMC Infect Dis

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Hand foot and mouth disease (HFMD) is caused by a variety of enteroviruses, and occurs in large outbreaks in which a small proportion of children deteriorate rapidly with cardiopulmonary failure. Determining which children are likely to deteriorate is difficult and health systems may become overloaded during outbreaks as many children require hospitalization for monitoring. Heart rate variability (HRV) may help distinguish those with more severe diseases but requires simple scalable methods to collect ECG data.We carried out a prospective observational study to examine the feasibility of using wearable devices to measure HRV in 142 children admitted with HFMD at a children’s hospital in Vietnam. ECG data were collected in all children. HRV indices calculated were lower in those with enterovirus A71 associated HFMD compared to those with other viral pathogens.HRV analysis collected from wearable devices is feasible in a low and middle income country (LMIC) and may help classify disease severity in HFMD.

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Continuous patient state attention model for addressing irregularity in electronic health records

Chauhan,

Thakur,

O’Donoghue

et al. 2024

BMC Med Inform Decis Mak

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Background Irregular time series (ITS) are common in healthcare as patient data is recorded in an electronic health record (EHR) system as per clinical guidelines/requirements but not for research and depends on a patient’s health status. Due to irregularity, it is challenging to develop machine learning techniques to uncover vast intelligence hidden in EHR big data, without losing performance on downstream patient outcome prediction tasks. Methods In this paper, we propose Perceiver, a cross-attention-based transformer variant that is computationally efficient and can handle long sequences of time series in healthcare. We further develop continuous patient state attention models, using Perceiver and transformer to deal with ITS in EHR. The continuous patient state models utilise neural ordinary differential equations to learn patient health dynamics, i.e., patient health trajectory from observed irregular time steps, which enables them to sample patient state at any time. Results The proposed models’ performance on in-hospital mortality prediction task on PhysioNet-2012 challenge and MIMIC-III datasets is examined. Perceiver model either outperforms or performs at par with baselines, and reduces computations by about nine times when compared to the transformer model, with no significant loss of performance. Experiments to examine irregularity in healthcare reveal that continuous patient state models outperform baselines. Moreover, the predictive uncertainty of the model is used to refer extremely uncertain cases to clinicians, which enhances the model’s performance. Code is publicly available and verified at https://codeocean.com/capsule/4587224. Conclusions Perceiver presents a computationally efficient potential alternative for processing long sequences of time series in healthcare, and the continuous patient state attention models outperform the traditional and advanced techniques to handle irregularity in the time series. Moreover, the predictive uncertainty of the model helps in the development of transparent and trustworthy systems, which can be utilised as per the availability of clinicians.

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Classification of Tetanus Severity in Intensive-Care Settings for Low-Income Countries Using Wearable Sensing

Cited by 5 publications

References 41 publications

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Improving Classification of Tetanus Severity for Patients in Low-Middle Income Countries Wearing ECG Sensors by Using a CNN-Transformer Network

Feasibility of wearable monitors to detect heart rate variability in children with hand, foot and mouth disease

Continuous patient state attention model for addressing irregularity in electronic health records

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