Hsin Hsiu

Background/Aim: Breast cancer (BC) remains a major health threat, highlighting the need for accessible screening. Characteristics of the cardiovascular system can be altered in BC patients. This study explored a noninvasive method combining arterial pulse-waveform analysis and classification to compare arterial pulse-waveform characteristics between BC patients ( n = 51)) and age-matched controls ( n = 76)). Methods: From one-minute radial waveform recordings, 40 harmonic indices (amplitude proportions, phase angles, and their variability) were derived. Significant differences in pulse-waveform indices were observed between groups, suggesting altered pulse-wave transmission conditions in BC patients. Two classifiers were compared: machine learning and a novel pulse-distribution analysis (PDA). Results: PDA demonstrated an acceptable discrimination with an AUC of 0.74, outperforming machine learning (AUC up to 0.58). Subgroup analysis by clinical factors such as TNM stage showed variable performance, with AUCs ranging from 0.66-0.93. Discrimination appeared to improve with disease progression (AUC 0.86 for stage IV), suggesting that tumor-induced vascular alterations may affect pulse-wave transmission. Conclusion: The primary contribution of this work is the identification of distinct pulse-waveform signatures in BC patients, providing a physiological basis for future development of noninvasive screening tools. The proposed method, characterized by its low cost and ease of use, warrants further validation in larger cohorts.

Background: Breast cancer (BC) treatment, while essential, often impairs quality of life (QoL), typically assessed through ­subjective reports. This single‑center, proof‑of‑concept observational study aimed to develop an objective and noninvasive monitoring method based on spectral analysis of the arterial blood pulse waveform (BPW). We investigated whether specific harmonic indices derived from the BPW could track physiological changes in BC patients following chemotherapy and correlate with patient-reported QoL improvements. Methods: Radial BPW signals were measured noninvasively in 41 BC patients before and after treatment, deriving harmonic indices including amplitude proportion and phase angle. QoL was assessed using the EORTC QLQ-C30 questionnaire. The pulse-distribution analysis (PDA) scoring model was used to classify patients based on their degree of improvement in Global-Health-Status scores. Results: Results showed significant post-chemotherapy decreases in specific harmonic indices, suggesting normalized vascular dynamics. The PDA scoring model effectively classified patients by their Global-Health-Status improvement, achieving an AUC of 0.87 for extreme groups. PDA effect scores demonstrated a significant positive correlation with QoL changes (R = 0.332), outperforming heart rate and its variability. Notably, subgroup analysis revealed this correlation was substantially stronger in early-stage patients (Stage I: R = 0.787). Crucially, PDA scores were not confounded by age. Conclusion: This exploratory study suggests that the pulse‑wave‑based approach may serve as a low-cost, portable tool for monitoring early physiological changes in BC patients, potentially complementing traditional subjective QoL assessments in clinical practice. The enhanced sensitivity observed in early‑stage disease warrants further investigation. These proof‑of‑concept findings are hypothesis‑generating and require validation in larger prospective studies.

AstraZeneca (AZ) vaccine is one of the most common vaccines against COVID-19 used globally. However, adverse reactions post-vaccination have been reported, including severe symptoms and cases of sudden death within several hours. Therefore, this study aimed to establish a database of spectral characteristics of blood pressure waveforms (BPWs) for the AZ vaccine and analyze reactions after vaccine administration using objective physiological signal and symptom analyses for identifying potential differences between heavy and slight groups defined in the study. In total, 24 participants were enrolled in the case-control study. BPW measurements were acquired pre- and post-vaccination. A questionnaire survey on side effects was conducted 5 days after vaccination. The related spectral characteristics of heavy and slight groups were acquired after Fourier transform analysis. Four types of harmonic indexes from BPW signals, including amplitude proportion (C _n ), coefficient of variation of C _n (CV _n ), phase angle (P _n ), and standard deviation of P _n (P _n _SD), were derived. The characteristics of harmonic indexes of arterial BPW for the AZ vaccine were in C₆ (P = .011), CV₂ (P = .027), P₅ (P = .009), and P₂_SD (P = .027) on the radial pulse. C₅ (P = .037), C₈ (P = .007), C₉ (P = .037), CV₅ (P = .015), CV₈ (P = .005), and CV₉ (P = .028) were significantly different at posttest between heavy and slight groups. In both pretest or posttest, C₈ was almost significantly different between slight and heavy groups. More parameters changed significantly post-vaccination, with more severe side effects. Most average values of posttest/pretest of CV _n and P _n _SD in the slight group exceeded 100%. All average values of posttest/pretest of CV _n and P _n _SD in the heavy group were smaller than 100%. This approach may enable prediction of the risk of reactions post-vaccination to determine suitability of the AZ vaccine and evaluation of side effect severity in vaccinated individuals using pulse analysis to ensure relevant precautions are taken.

The impact of sleep disorders (SDs), particularly sleep apnea (SA), on the development of colorectal cancer (CRC) has been the subject of significant research. However, the potential contribution of other SDs to the incidence of CRC remains unexplored. The objective of this study was to examine the effects of SDs on the risk of developing CRC. This study assessed CRC risk among individuals diagnosed with SDs compared with age- and sex-matched unaffected individuals. A longitudinal, nationwide, population-based cohort study was conducted using data from the Taiwan National Health Insurance Research Database (NHIRD) encompassing 177,707 individuals diagnosed with SDs and 177,707 matched controls. Cox proportional hazard regression analysis was used to determine the relative increased risk of CRC in individuals with SDs and specific subgroups of SDs. The CRC incidences were 1.32-fold higher (95% CI 1.23–1.42) in the overall SD cohort, 1.17-fold higher (95% CI 0.82–1.68) in the SA cohort, 1.42-fold higher (95% CI 1.31–1.55) in the insomnia cohort, 1.27-fold higher (95% CI 1.17–1.38) in the sleep disturbance cohort, and 1.00-fold higher (95% CI 0.77–1.29) in the other SD cohort, after adjusting for age, sex, and comorbidities.

The vascular structure and function are potentially useful biomarkers for tumor detection. Treatment with chemotherapeutic agents may impair vascular function and increase the risk of cardiovascular disease. This study aimed to use noninvasive pulse waveform measurements to identify differences in the frequency-domain indices of the pulse waveform in breast-cancer patients following anthracycline chemotherapy between with (Group KSY) and without (Group NKSY) receiving Kuan-Sin-Yin (KSY) treatment. Radial blood pressure waveform (BPW) signals were measured noninvasively for 3 minutes in 31 patients, and the FACT-G, BFI-T, and EORTC QLQ-C30 questionnaires were administered. The following pulse indices were calculated for 10 harmonics: the amplitude proportion and its coefficient of variation, and the phase angle and its standard deviation. The changes in spectral BPW indices were more prominent in Group NKSY than in Group KSY, especially for the decreases in BPW variability indices. Scores on the FACT-G, BFI-T, and EORTC QLQ-C30 questionnaires suggested that the quality of life following chemotherapy was better in Group KSY. The identified decreases in pulse variability indices could be related to the greater impairment of regulatory activities in Group NKSY. The present findings may be meaningful in developing techniques with advantages such as being noninvasive and time-saving to evaluate the blood supply and physiological conditions following chemotherapy or other treatment strategies in cancer patients.

Background and Aims: Running can induce advantageous cardiovascular effects such as improved arterial stiffness and blood-supply perfusion. However, the differences between the vascular and blood-flow perfusion conditions under different levels of endurance-running performance remains unclear. The present study aimed to assess the vascular and blood-flow perfusion conditions among 3 groups (44 male volunteers) according to the time taken to run 3 km: Level 1, Level 2, and Level 3. Methods: The radial blood pressure waveform (BPW), finger photoplethygraphy (PPG), and skin-surface laser-Doppler flowmetry (LDF) signals of the subjects were measured. Frequency-domain analysis was applied to BPW and PPG signals; time- and frequency-domain analyses were applied to LDF signals. Results: Pulse waveform and LDF indices differed significantly among the three groups. These could be used to evaluate the advantageous cardiovascular effects provided by long-term endurance-running training, such as vessel relaxation (pulse waveform indices), improvement in blood supply perfusion (LDF indices), and changes in cardiovascular regulation activities (pulse and LDF variability indices). Using the relative changes in pulse-effect indices, we achieved almost perfect discrimination between Level 3 and Level 2 (AUC = 0.878). Furthermore, the present pulse waveform analysis could also be used to discriminate between the Level-1 and Level-2 groups. Conclusions: The present findings contribute to the development of a noninvasive, easy-to-use, and objective evaluation technique for the cardiovascular benefits of prolonged endurance-running training.

Sarcopenia is defined as decreased skeletal muscle mass and function, and is an important cause of frailty in the elderly, also being associated with vascular lesions and poor microcirculation. The present study aimed to combine noninvasive pulse measurements, frequency-domain analysis, and machine learning (ML) analysis (1) to determine the effects on the pulse waveform induced by sarcopenia and (2) to develop discriminating models for patients with possible sarcopenia. Radial blood pressure waveform (BPW) signals were measured noninvasively for 1 min in 133 subjects who visited Tri-Service General Hospital for geriatric health checkups. They were assigned to a robust group and a possible-sarcopenia group that combined dynapenia, presarcopenia, and sarcopenia. Two classification methods were used: ML analysis and a self-developed scoring system that used 40 harmonic pulse indices as features: amplitude proportions and their coefficients of variation, and phase angles and their standard deviations. Significant differences were found in several spectral indices of the BPW between possible-sarcopenia and robust subjects. Threefold cross-validation results indicated excellent discrimination performance, with AUC equaling 0.77 when using LDA and 0.83 when using our scoring system. The present noninvasive and easy-to-use measurement and analysis method for detecting sarcopenia-induced changes in the arterial pulse transmission condition could aid the discrimination of possible sarcopenia.

Pulse measurements made using wearable devices can aid the monitoring of human physiological condition. Accurate estimation of waveforms is often difficult for nonexperts; motion artifacts may occur during tonometry measurements when the skin–sensor contact pressure is insufficient. An alternative approach is to extract only high-quality pulses for use in index calculations. The present study aimed to determine the effectiveness of using machine-learning analysis in discriminating between high-quality and low-quality pulse waveforms induced by applying different contact pressures. Radial blood pressure waveform (BPW) signals were measured noninvasively in healthy young subjects using a strain-gauge transducer. One-minute-long trains of pulse data were measured when applying the appropriate contact pressure (67.80 ± 1.55 mmHg) and a higher contact pressure (151.80 ± 3.19 mmHg). Eight machine-learning algorithms were employed to evaluate the following 40 harmonic pulse indices: amplitude proportions and their coefficients of variation and phase angles and their standard deviations. Significant differences were noted in BPW indices between applying appropriate and higher skin–surface contact pressures. The present appropriate contact pressure could not only provide a suitable holding force for the wearable device but also helped to maintain the physiological stability of the underlying tissues. Machine-learning analysis provides an effective method for distinguishing between the high-quality and low-quality pulses with excellent discrimination performance (leave-one-subject-out test: random-forest AUC = 0.96). This approach will aid the development of an automatic screening method for waveform quality and thereby improve the noninvasive acquisition reliability. Other possible interfering factors in practical applications can also be systematically studied using a similar procedure.