Binary Classifiers for Data Integrity Detection in Wearable IoT Edge Devices

John, Arlene; Panicker, Rajesh C.; Cardiff, Barry; Lian, Yong; John, Deepu

doi:10.1109/ojcas.2020.3009520

Cited by 13 publications

(4 citation statements)

References 30 publications

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“…However, their widespread adoption limits the ability of intelligent learning networks to perform multiple tasks and ensure data integrity. To this end, some recent efforts have focused on building automated security systems based on data impact anomaly detection and classification, checking the integrity of electrocardiogram (ECG) data [197], tracing the root cause of integrity compromise [198], and leveraging blockchain to improve the data integrity of food and drug administration (FDA)-approved medical wearables [199].…”

Section: Integritymentioning

confidence: 99%

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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Recent medical applications are largely dominated by the application of Machine Learning (ML) models to assist expert decisions, leading to disruptive innovations in radiology, pathology, genomics, and hence modern healthcare systems in general. Despite the profitable usage of AI-based algorithms, these data-driven methods are facing issues such as the scarcity and privacy of user data, as well as the difficulty of institutions exchanging medical information. With insufficient data, ML is prevented from reaching its full potential, which is only possible if the database consists of the full spectrum of possible anatomies, pathologies, and input data types. To solve these issues, Federated Learning (FL) appeared as a valuable approach in the medical field, allowing patient data to stay where it is generated. Since an FL setting allows many clients to collaboratively train a model while keeping training data decentralized, it can protect privacy-sensitive medical data. However, FL is still unable to deliver all its promises and meets the more stringent requirements (e.g., latency, security) of a healthcare system based on multiple Internet of Medical Things (IoMT). For example, although no data are shared among the participants by definition in FL systems, some security risks are still present and can be considered as vulnerabilities from multiple aspects. This paper sheds light upon the emerging deployment of FL, provides a broad overview of current approaches and existing challenges, and outlines several directions of future work that are relevant to solving existing problems in federated healthcare, with a particular focus on security and privacy issues.

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Section: Integritymentioning

confidence: 99%

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Aouedi

Sacco

Piamrat

et al. 2023

IEEE J. Biomed. Health Inform.

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“…The model complexity was calculated in terms of the number of multiplications and additions required for sleep apnea detection per second [18]. Prediction with the model M1 requires 6534116 multiplications and 656647 additions.…”

Section: B Complexity Calculationmentioning

confidence: 99%

A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors

John

Cardiff

John

2021

2021 IEEE International Symposium on Circuits and Systems (ISCAS)

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Internet of Things (IoT) enabled wearable sensors for health monitoring are widely used to reduce the cost of personal healthcare and improve quality of life. The sleep apneahypopnea syndrome, characterized by the abnormal reduction or pause in breathing, greatly affects the quality of sleep of an individual. This paper introduces a novel method for apnea detection (pause in breathing) from electrocardiogram (ECG) signals obtained from wearable devices. The novelty stems from the high resolution of apnea detection on a second-by-second basis, and this is achieved using a 1-dimensional convolutional neural network for feature extraction and detection of sleep apnea events. The proposed method exhibits an accuracy of 99.56% and a sensitivity of 96.05%. This model outperforms several lower resolution state-of-the-art apnea detection methods. The complexity of the proposed model is analyzed. We also analyze the feasibility of model pruning and binarization to reduce the resource requirements on a wearable IoT device. The pruned model with 80% sparsity exhibited an accuracy of 97.34% and a sensitivity of 86.48%. The binarized model exhibited an accuracy of 75.59% and sensitivity of 63.23%. The performance of low complexity patient-specific models derived from the generic model is also studied to analyze the feasibility of retraining existing models to fit patient-specific requirements. The patient-specific models on average exhibited an accuracy of 97.79% and sensitivity of 92.23%. The source code for this work is made publicly available.

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“…The computational complexities of the three networks were calculated in terms of the number of multiplications and additions required for each second [42]. The computations involved in the convolution layers were calculated based on simple filtering calculation count and without assuming a fast Fourier transform approach [43].…”

Section: Complexity Analysismentioning

confidence: 99%

“…For the Max Pooling layers, the operation of selecting the maximum is approximated to an addition operation. For the dense layers, the calculations are carried out as discussed in [42]. Detection of sleep apnea events with ECG signal using M 1 1 requires 6534116 multiplications and 6546647 additions [37], and with SpO2 signal using M 1 2 requires 1270016 multiplications and 1272876 additions [38].…”

Section: Complexity Analysismentioning

confidence: 99%

Multimodal Multiresolution Data Fusion Using Convolutional Neural Networks for IoT Wearable Sensing

John

Nundy²,

Cardiff

et al. 2021

IEEE Trans. Biomed. Circuits Syst.

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With advances in circuit design and sensing technology, the acquisition of data from a large number of Internet of Things (IoT) sensors simultaneously to enable more accurate inferences has become mainstream. In this work, we propose a novel convolutional neural network (CNN) model for the fusion of multimodal and multiresolution data obtained from several sensors. The proposed model enables the fusion of multiresolution sensor data, without having to resort to padding/ resampling to correct for frequency resolution differences even when carrying out temporal inferences like high-resolution event detection. The performance of the proposed model is evaluated for sleep apnea event detection, by fusing three different sensor signals obtained from UCD St. Vincent University Hospital's sleep apnea database. The proposed model is generalizable and this is demonstrated by incremental performance improvements, proportional to the number of sensors used for fusion. A selective dropout technique is used to prevent overfitting of the model to any specific high-resolution input, and increase the robustness of fusion to signal corruption from any sensor source. A fusion model with electrocardiogram (ECG), Peripheral oxygen saturation signal (SpO2), and abdominal movement signal achieved an accuracy of 99.72% and a sensitivity of 98.98%. Energy per classification of the proposed fusion model was estimated to be approximately 5.61 µJ for on-chip implementation. The feasibility of pruning to reduce the complexity of the fusion models was also studied.

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Binary Classifiers for Data Integrity Detection in Wearable IoT Edge Devices

Cited by 13 publications

References 30 publications

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

Handling Privacy-Sensitive Medical Data With Federated Learning: Challenges and Future Directions

A 1D-CNN Based Deep Learning Technique for Sleep Apnea Detection in IoT Sensors

Multimodal Multiresolution Data Fusion Using Convolutional Neural Networks for IoT Wearable Sensing

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