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Cardiopulmonary monitoring is important and useful for diagnosing and managing multiple conditions, such as stress and sleep disorders. Wearable ambulatory systems can provide continuous, comfortable, and inexpensive means for monitoring; it always has been a research subject in recent years. Being simple and cost-effective, electrocardiogram-based commercial products can be found in the market that provides cardiac diagnostic information for assessment, including heart rate measurement and atrial fibrillation identification. Based on a data-driven and self-adaptive approach, this study aims to estimate heart rate and respiratory rate simultaneously from one lead electrocardiogram signal. In contrast to ensemble empirical mode decomposition with principle component analysis, performed in the time domain, our method uses spectral data fusion, together with intrinsic mode functions using ensemble empirical mode decomposition obtains a more accurate heart rate and respiratory rate. Equipped with a rule-based selection of defined frequency levels for respiratory rate (RR) estimation, the proposed method obtains (0.92, 1.32) beat per minute for the heart rate and (2.20, 2.92) breath per minute for the respiratory rate as their mean absolute error and root mean square error, respectively outperforming other existing methods.

Model predictive current controllers (MPCCs) are widely applied in motor drive control and operations. To date, however, the presence of large current errors in conventional predictive current control remains a significant predicament, due to harmonic distortions and current ripples. Naturally, noticeable current estimation inaccuracies lead to poor performance. To improve the above situation, a modulated model predictive current controller (MMPCC) is proposed for interior permanent-magnet synchronous motors (IPMSMs) in this paper. Two successive voltage vectors will be applied in a sampling period to greatly boost the number of candidate switching modes from seven to thirteen. A cost function, which is defined as the quadratic sum of current prediction errors, is employed to find an optimal switching mode and an optimized duty ratio to be applied in the next sampling period, such that the cost value is minimal. The effectiveness of the proposed method is verified through eight experiments using a TMS320F28379D microcontroller, and performance comparisons are made against an existing MPCC. In terms of quantitative improvements made to the MPCC, the proposed MMPCC reduces its current ripple and total harmonic distortion (THD) by, on average, 27.17% and 21.84%, respectively.

For current control in power conversion and motor drive systems, there exist three classic methods in the literature and they are the hysteresis current control (HCC), the sine pulse-width modulation (SPWM), and the space vector pulse width modulation (SVPWM). HCC is easy to implement, but has relatively large current harmonic distortion as the disadvantage. On the other hand, the SPWM and SVPWM use modulation technique, commonly together with at least one proportional-integral (PI) regulator to reduce load current ripples, and hence demanding more computation time. This paper aims to improve the performance of a recently proposed new current control method—the single-voltage-vector model predictive current control (SVV-MPCC), for synchronous reluctance motor (SynRMs) drives. To that end, a dual-voltage-vector model-free predictive current control (DVV-MFPCC) for SynRMs is proposed. Unlike the SVV-MPCC that applies only a single voltage vector per sampling period, the proposed DVV-MFPCC is capable of providing two successive segmentary current predictions in the next sampling period through all possible combinations from any two candidate switching states increasing the number of applicable switching modes from seven to nineteen and reducing the prediction error effectively. Moreover, the new control does not utilize any parameters of the SynRM nor its mathematical model. The performance is effectively enhanced compared to that of SVV-MPCC. The working principle of the DVV-MFPCC will be detailed in this paper. Finally, the SVV-MPCC, the single-voltage-vector model-free predictive current control (SVV-MFPCC), the dual-voltage-vector model predictive current control (DVV-MPCC), and the DVV-MFPCC are realized to control the stator currents of SynRM through a 32-bit microcontroller TMS320F28377S. Experimental results are provided to validate the new method and verify that the DVV-MFPCC performs better than do the SVV-MPCC, the SVV-MFPCC, and the DVV-MPCC.

Background The T790M mutation is the major resistance mechanism to first- and second-generation TKIs in EGFR-mutant NSCLC. This study aimed to investigate the utility of droplet digital PCR (ddPCR) for detection of T790M in plasma circulating tumor DNA (ctDNA), and explore its impact on prognosis. Methods This prospective study enrolled 80 advanced lung adenocarcinoma patients treated with gefitinib, erlotinib, or afatinib for TKI-sensitizing mutations between 2015 and 2019. Plasma samples were collected before TKI therapy and at tri-monthly intervals thereafter. Genotyping of ctDNA for T790M was performed using a ddPCR EGFR Mutation Assay. Patients were followed up until the date of death or to the end of 2021. Results Seventy-five of 80 patients experienced progressive disease. Fifty-three (71%) of 75 patients underwent rebiopsy, and T790M mutation was identified in 53% (28/53) of samples. Meanwhile, plasma ddPCR detected T790M mutation in 23 (43%) of 53 patients. The concordance rate of T790M between ddPCR and rebiopsy was 76%, and ddPCR identified 4 additional T790M-positive patients. Ten (45%) of 22 patients who did not receive rebiopsy tested positive for T790M by ddPCR. Serial ddPCR analysis showed the time interval from detection of plasma T790M to objective progression was 1.1 (0–4.1) months. Compared to 28 patients with rebiopsy showing T790M, the overall survival of 14 patients with T790M detected solely by ddPCR was shorter(41.3 [95% CI, 36.6–46.0] vs. 26.6 months [95% CI, 9.9–43.3], respectively). Conclusion Plasma ddPCR-based genotyping is a useful technology for detection and monitoring of the key actionable genomic alteration, namely, T790M, in patients treated with gefitinib, erlotinib, or afatinib for activating mutations, to achieve better patient care and outcome.

The use of bodyweight unloading force control on a treadmill with therapist manual assistance for gait training imposes constraints on natural walking. It influences the patient’s training effect for a full range of natural walks. This study presents a prototype and a safety controller for a mobile rehabilitation robot (MRR). The prototype integrates an autonomous mobile bodyweight support system (AMBSS) with a lower-limb exoskeleton system (LES) to simultaneously achieve natural over-ground gait training and motion relearning. Human-centered rehabilitation robots must guarantee the safety of patients in the presence of significant tracking errors. It is difficult for traditional stiff controllers to ensure safety and excellent tracking accuracy concurrently, because they cannot explicitly guarantee smooth, safe, and overdamped motions without overshoot. This paper integrated a linear extended state observer (LESO) into proxy-based sliding mode control (ILESO-PSMC) to overcome this problem. The LESO was used to observe the system’s unknown states and total disturbance simultaneously, ensuring that the “proxy” tracks the reference target accurately and avoids the unsafe control of the MRR. Based on the Lyapunov theorem to prove the closed-loop system stability, the proposed safety control strategy has three advantages: (1) it provides an accurate and safe control without worsening tracking performance during regular operation, (2) it guarantees safe recoveries and overdamped properties after abnormal events, and (3) it need not identify the system model and measure unknown system states as well as external disturbance, which is quite difficult for human–robot interaction (HRI) systems. The results demonstrate the feasibility of the proposed ILESO-PSMC for MRR. The experimental comparison also indicates better safety performance for the ILESO-PSMC than for the conventional proportional–integral–derivative (PID) control.

A dynamic time warping (DTW) algorithm has been suggested for the purpose of devising a motion-sensitive microelectronic system for the realization of remote motion abnormality detection. In combination with an inertial measurement unit (IMU), the algorithm is potentially applicable for remotely monitoring patients who are at risk of certain exceptional motions. The fixed interval signal sampling mechanism has normally been adopted when devising motion detection systems; however, dynamically capturing the particular motion patterns from the IMU motion sensor can be difficult. To this end, the DTW algorithm, as a kind of nonlinear pattern-matching approach, is able to optimally align motion signal sequences tending towards time-varying or speed-varying expressions, which is especially suitable to capturing exceptional motions. Thus, this paper evaluated this kind of abnormality detection using the proposed DTW algorithm on the basis of its theoretical fundamentals to significantly enhance the viability of the methodology. To validate the methodological viability, an artificial neural network (ANN) framework was intentionally introduced for performance comparison. By incorporating two types of designated preprocessors, i.e., a DFT interpolation preprocessor and a convolutional preprocessor, to equalize the unequal lengths of the matching sequences, two kinds of ANN frameworks were enumerated to compare the potential applicability. The comparison eventually confirmed that the direct template-matching DTW is excellent in practical application for the detection of time-varying or speed-varying abnormality, and reliably captures the consensus exceptions.

Background Previous studies show that using 12‐lead electrocardiogram (ECG) or 24‐h ECG monitor for the detection of cardiac arrhythmia events in patients with stroke or syncope is ineffective. Hypothesis The 14‐day continuous ECG patch has higher detection rates of arrhythmias compared with conventional 24‐h ECG monitoring in patients with ischemic stroke or syncope. Methods This cross‐sectional study of patients with newly diagnosed ischemic stroke or syncope received a 24‐h ECG monitoring and 14‐day continuous cardiac monitoring patch and the arrhythmia events were measured. Results This study enrolled 83 patients with ischemic stroke or syncope. The detection rate of composite cardiac arrhythmias was significantly higher for the 14‐day ECG patch than 24‐h Holter monitor (69.9% vs. 21.7%, p = .006). In patients with ischemic stroke, the detection rates of cardiac arrhythmias were 63.4% for supraventricular tachycardia (SVT), 7% for ventricular tachycardia (VT), 5.6% for atrial fibrillation (AF), 4.2% for atrioventricular block (AVB), and 1.4% for pause by 14‐day ECG patch, respectively. The significant difference in arrhythmic detection rates were found for SVT (45.8%), AF (6%), pause (1.2%), AVB (2.4%), and VT (9.6%) by 14‐day ECG patch but not by 24‐h Holter monitor in patients with ischemic stroke or syncope. Conclusions A 14‐day ECG patch can be used on patients with ischemic stroke or syncope for the early detection of AF or other cardiac arrhythmia events. The patch can be helpful for physicians in planning medical or mechanical interventions of patients with ischemic stroke and occult AF. This study compared the performance of 14‐day continuous ECG patch with conventional 24‐h ECG monitoring for the detection of arrhythmias in 83 patients with ischemic stroke or syncope. The detection rate of composite cardiac arrhythmias was significantly higher for the 14‐day ECG patch than 24‐h Holter monitor (69.9% vs. 21.7%, p = .006). In patients with ischemic stroke, the detection rates of cardiac arrhythmias were 63.4% for supraventricular tachycardia (SVT), 7% for ventricular tachycardia (VT), 5.6% for atrial fibrillation (AF), 4.2% for atrioventricular block (AVB), and 1.4% for pause by 14‐day ECG patch, respectively. A 14‐day ECG patch can be used on patients with ischemic stroke or syncope for the early detection of AF or other cardiac arrhythmia events. The patch can be helpful for physicians in planning medical or mechanical interventions of patients with ischemic stroke and occult AF.

Background Ventricular tachyarrhythmia (VT) occasionally occurred in patients with Takotsubo cardiomyopathy (TC). Two convex‐type ST elevations were significantly related to VT in coronary artery disease. Methods This study assessed the correlation between VT and critical ECG patterns, as well other independent predictive factors of in‐hospital outcome. Fifty‐five consecutive patients fulfilled the diagnostic criteria of Takotsubo Italian Network (TIN) were retrospectively enrolled. The patients were classified into two groups according to their critical ECG patterns and VT occurrence. In‐hospital outcomes and influencing factors were analyzed. Results The incidence of VT was higher in the critical ECG group than in the Noncritical ECG group (43.8% vs. 2.6%, p < 0.001). In‐hospital death was more common in the critical ECG group than in the Noncritical ECG group (25.0% vs. 5.1%, p = 0.032). The composite end‐point (combined VT and in‐hospital death) revealed significant differences between these two groups (50.0% vs 7.7%, p < 0.001). Multi‐variate analysis proved critical ECG type as one independent risk factor of VT (odds ratio [OR] = 61.8, p = 0.009) and the composite end‐point (OR = 12.4, p = 0.007). The prolong QRS width ( ≥ 105 ms) was another independent factor for predicting VT (OR = 1.06, p = 0.022) and composite end‐point (OR = 1.05, p = 0.017). Conclusions Critical ECG types including tombstoning ST elevation and lambda‐wave ST elevation have strong impact on short‐term outcomes. Additionally, conduction disturbance with prolong QRS ≥ 105 ms also has independent predicting role for poor prognosis. Ventricular tachyarrhythmia (VT) occasionally occurred in patients with Takotsubo cardiomyopathy (TC). This study assessed the correlation between VT and critical ECG patterns. Critical ECG types including tombstoning ST elevation and lambda‐wave ST elevation have strong impact on short‐term outcomes. Summary Key findings Both critical ECG types and prolong QRS 105 ms had independent predicting role for poor prognosis in patients with stress‐induced cardiomyopathy. What is known and what is new? An lambda‐wave ST elevation and tombstoning ST elevation may predict malignant arrhythmias in patients with CAD and STEMI. For patients with stress‐induced cardiomyopathy, the critical ECG types including tombstoning ST elevation and lambda‐wave ST elevation had strong impact for VT and in‐hospital death. What is the implication, and what should change now? Above abnormal ECG signs identifying high‐risk patients and implementing close monitoring and aggressive interventions.

With the rapid development of science and technology, the living habits of people have also changed from those in the past; the diet, living environment, various life pressures, etc., all overwhelm the body and mind, meaning that, nowadays, more people are suffering from mental illness and cardiovascular disease year on year. Therefore, a non-contact measurement of heart rate and heart rate variability (HRV) is proposed to assist physicians in diagnosing symptoms related to mental illness and cardiovascular disease. In this paper, continuous images are obtained by general network cameras with non-contact, facial feature points and regions of interest (ROI) employed to track faces and regional images; HRV parameters were analyzed with the green wavelength of RGB color space. The artifact signal is eliminated by a hybrid algorithm of independent component analysis (ICA) and particle swarming optimization (PSO). Finally, the values of heart rate and HRV are obtained with signal processes of using band-pass filter, fast Fourier transform (FFT), and power spectrum analysis in the time and frequency domains, respectively. The non-contact measurement performance of the proposed method can effectively not only avoid infection doubts and obtain heart rate and HRV quickly, but also provide better physiological parameters, root mean square error (RMSE), and mean absolute percentage error (MAPE), than those of recent published papers.

Purpose To enhance the predictive risk model for all-cause mortality in individuals with Type 2 Diabetes (T2DM) and prolonged Atherosclerotic Cardiovascular Disease (ASCVD) risk factors. Despite the utility of the Coronary Artery Calcium (CAC) score in assessing cardiovascular risk, its capacity to predict all-cause mortality remains limited. Methods A retrospective cohort study included 1929 asymptomatic T2DM patients with ASCVD risk factors, aged 40–80. Variables encompassed demographic attributes, clinical parameters, CAC scores, comorbidities, and medication usage. Factors predicting all-cause mortality were selected to create a predictive scoring system. By using stepwise selection in a multivariate Cox proportional hazards model, we divided the patients into three risk groups. Results In our analysis of all-cause mortality in T2DM patients with extended ASCVD risk factors over 5 years, we identified significant risk factors, their adjusted hazard ratios (aHR), and scores: e.g., CAC score > 1000 (aHR: 1.57, score: 2), CAC score 401–1000 (aHR: 2.05, score: 2), and more. These factors strongly predict all-cause mortality, with varying risk groups (e.g., very low-risk: 2.0%, very high-risk: 24.0%). Significant differences in 5-year overall survival rates were observed among these groups (log-rank test < 0.001). Conclusion The Poh-Ai Predictive Scoring System excels in forecasting mortality and cardiovascular events in individuals with Type 2 Diabetes Mellitus and extended ASCVD risk factors.