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The rapid advancements in Artificial Intelligence of Things (AIoT) are pivotal for the healthcare sector, especially as the world approaches an aging society which will be reached by 2050. This paper presents an innovative AIoT-enabled data fusion system implemented at the CMUH Respiratory Intensive Care Unit (RICU) to address the high incidence of medical errors in ICUs, which are among the top three causes of mortality in healthcare facilities. ICU patients are particularly vulnerable to medical errors due to the complexity of their conditions and the critical nature of their care. We introduce a four-layer AIoT architecture designed to manage and deliver both real-time and non-real-time medical data within the CMUH-RICU. Our system demonstrates the capability to handle 22 TB of medical data annually with an average delay of 1.72 ms and a bandwidth of 65.66 Mbps. Additionally, we ensure the uninterrupted operation of the CMUH-RICU with a three-node streaming cluster (called Kafka), provided a failed node is repaired within 9 h, assuming a one-year node lifespan. A case study is presented where the AI application of acute respiratory distress syndrome (ARDS), leveraging our AIoT data fusion approach, significantly improved the medical diagnosis rate from 52.2% to 93.3% and reduced mortality from 56.5% to 39.5%. The results underscore the potential of AIoT in enhancing patient outcomes and operational efficiency in the ICU setting. [ABSTRACT FROM AUTHOR]

In next-generation communications, massive machine-type communications (mMTC) induce severe burden on base stations. To address such an issue, automatic modulation classification (AMC) can help to reduce signaling overhead by blindly recognizing the modulation types without handshaking. Thus, it plays an important role in future intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, DL-based approaches suffer from heavy training overhead, memory overhead, and computational complexity, which severely hinder practical applications for resource-limited scenarios, such as Internet-of-Things (IoT) networks and unmanned aerial vehicle (UAV)-aided systems. Furthermore, the overhead of online retraining under time-varying fading channels has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with a channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from the polar domain with historical data information can approach near-optimal performance while reducing training overhead by 99.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying this lightweight NN-CE in a time-varying fading channel, two efficient mechanisms of online retraining are proposed, which can reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency and lightness. The lightweight and efficient learning features of the proposed mechanism will be very attractive for future resource-constrained/aware IoT and Vehicle-to-Everything (V2X) applications. [ABSTRACT FROM AUTHOR]

Compressive sensing (CS) is attractive in long-term electrocardiography (ECG) telemonitoring to extend life-time for resource-constrained wireless wearable sensors. However, the availability of transmitted personal information has posed great concerns for potential privacy leakage. Moreover, the traditional CS-based security frameworks focus on secured signal recovery instead of privacy-preserving data analytics; hence, they provide only computational secrecy and have impractically high complexities for decryption. In this paper, to protect privacy from an information-theoretic perspective while delivering the classification capability, we propose a low-complexity framework of Privacy-Preserving Compressive Analysis (PPCA) based on subspace-based representation. The subspace-based dictionary is used for both encrypting and decoding the CS measurements online, and it is built by dividing signal space into discriminative and complementary subspace offline. The encrypted signal is unreconstructable even if the eavesdropper cracks the measurement matrix and the dictionary. PPCA is implemented in ECG-based atrial fibrillation detection. It can reduce the mutual information by 1.98 bits via encrypting measurements with signal-dependent noise at 1 dB, while the classification accuracy remains 96.05% with the decoding matrix. Furthermore, by decoding via matrix–vector product, rather than sparse coding, this computational complexity of PPCA is 341 times fewer compared with the traditional CS-based security. [ABSTRACT FROM AUTHOR]

Background: Chronotropic incompetence (CI) severely limits exercise tolerance due to impaired heart rate responses. This study investigated whether pacemaker with closed-loop stimulation (DDD-CLS) pacing, which provides rate acceleration in response to exertion, could enhance lung function and cardiopulmonary capacity compared pacemaker without CLS pacing in patients with CI., Methods: This randomized crossover trial included 32 patients with CI who were compared to each CLS and DDD pacing over 2 months. Spirometry was used to assess the forced vital capacity (FVC). Cycling-based cardiopulmonary exercise testing (CPET) measured peak oxygen uptake (peak VO 2 )., Results: DDD-CLS pacing yielded significantly higher FVC (2.2 ± 0.8 L) versus DDD pacing (2.0 ± 0.7 L), p = 0.036, a 3.2% improvement. Moreover, those in the DDD-CLS mode showed a significantly higher FEV1 of 1.8 ± 0.6 L compared to the DDD mode of 1.7 ± 0.5 L (p = 0.03). Compared with DDD pacing, DDD-CLS pacing also achieved superior peak VO 2 (14.9 ± 4.0 vs 12.2 ± 3.7 mlO 2 /kg/min, p < 0.001) and peak heart rate (106.9 ± 9.7 vs 98.3 ± 19.8 bpm, p = 0.013). DDD-CLS and DDD pacing modes showed significant correlations between FVC and peak VO 2 and between peak VO 2 and peak heart rate during CPET., Conclusions: Compared with DDD mode, DDD-CLS pacing provided physiological chronotropic support to improve cardiopulmonary function during exertion, which enhanced lung capacity in patients with CI., Competing Interests: Declaration of competing interest None declared., (Copyright © 2024 Formosan Medical Association. Published by Elsevier B.V. All rights reserved.)