Blood pressure estimation based on pulse rate variation in a certain period

Mental workload task modeled on office work: Focusing on the flow state for well-being

This research aimed to objectively evaluate the optimal state of desk work (flow state) through physiological measurements and use the data to support workers' mental health and well-being. We suppose that the flow state evaluation in real-time can contribute to a concentrated work environment, improved work efficiency, and stabilize worker's minds. This study reports on the development of the mental task modeled on daily work for the physiological measurement experiment. In the first phase of the research, a field survey was conducted with 55 desk workers to understand the details of their jobs and develop suitable mental tasks. Further, the relationship between daily work content and subjective stress was clarified. In the second phase, based on the results of the field survey, a task inducing the flow state was developed for practical use. Through empirical experiments with 35 participants (22 adults and 13 students), the developed task was evaluated for its usefulness and possible issues by examining the relationships among subjective assessment, task performance, degree of flow state, and individual characteristics. The study results showed that the proposed mental task developed in this study constitutes practical work that can be used for concentrated and goal-directed efforts. The task also demonstrated the property of inducing a flow state. Further, the results suggest that it is necessary to adjust the task difficulty level and implement effective feedback methods to induce the flow state more effectively.

Ensemble averaging for categorical variables: Validation study of imputing lost data in 24-h recorded postures of inpatients

Acceleration sensors are widely used in consumer wearable devices and smartphones. Postures estimated from recorded accelerations are commonly used as features indicating the activities of patients in medical studies. However, recording for over 24 h is more likely to result in data losses than recording for a few hours, especially when consumer-grade wearable devices are used. Here, to impute postures over a period of 24 h, we propose an imputation method that uses ensemble averaging. This method outputs a time series of postures over 24 h with less lost data by calculating the ratios of postures taken at the same time of day during several measurement-session days. Whereas conventional imputation methods are based on approaches with groups of subjects having multiple variables, the proposed method imputes the lost data variables individually and does not require other variables except posture. We validated the method on 306 measurement data from 99 stroke inpatients in a hospital rehabilitation ward. First, to classify postures from acceleration data measured by a wearable sensor placed on the patient's trunk, we preliminary estimated possible thresholds for classifying postures as 'reclining' and 'sitting or standing' by investigating the valleys in the histogram of occurrences of trunk angles during a long-term recording. Next, the imputations of the proposed method were validated. The proposed method significantly reduced the missing data rate from 5.76% to 0.21%, outperforming a conventional method.

Impact of fluid balance and blood transfusion during extracorporeal circulation on outcome for acute type A aortic dissection surgery

BACKGROUND

In thoracic aortic surgery, fluid replacement and blood transfusion during extracorporeal circulation (ECC) are associated with increased coagulopathy, elevated inflammatory response, and end-organ dysfunction. The optimal strategy has not been established in this regard. The aim of this study was to evaluate the effect of the fluid balance during ECC in thoracic aortic dissection surgery on outcome.

METHODS

Between 2009 and 2020, 358 patients suffering from acute type A aortic dissection (ATAAD) underwent aortic surgery at our heart center. In-hospital mortality, major complications (postoperative stroke, respiratory failure, heart failure, acute renal failure), and follow-up mortality were assessed. Logistic regression analysis was used to identify whether fluid balance and blood transfusion during ECC were risk factors for occurring adverse events.

RESULTS

The in-hospital mortality amounted to 20.4%. Major complications included temporary neurologic deficit in 13.4%, permanent neurologic deficit in 6.1%, acute renal failure in 32.7%, prolonged ventilation for respiratory failure in 17.9%, and acute heart failure in 10.9% of cases. At a mean of 42 months after discharge of 285 survivors, follow-up mortality was 13.3%. Multivariate analysis revealed major complications as well as the risk of in-hospital and follow-up mortality to increase with fluid balance and blood transfusion during ECC.

CONCLUSIONS

Fluid balance and blood transfusion during ECC present with predictive potential concerning the risk of postoperative adverse events.

Discussion of Cuffless Blood Pressure Prediction Using Plethysmograph Based on a Longitudinal Experiment: Is the Individual Model Necessary?

Using the Plethysmograph (PPG) signal to estimate blood pressure (BP) is attractive given the convenience and possibility of continuous measurement. However, due to the personal differences and the insufficiency of data, the dilemma between the accuracy for a small dataset and the robustness as a general method remains. To this end, we scrutinized the whole pipeline from the feature selection to regression model construction based on a one-month experiment with 11 subjects. By constructing the explanatory features consisting of five general PPG waveform features that do not require the identification of dicrotic notch and diastolic peak and the heart rate, three regression models, which are partial least square, local weighted partial least square, and Gaussian Process model, were built to reflect the underlying assumption about the nature of the fitting problem. By comparing the regression models, it can be confirmed that an individual Gaussian Process model attains the best results with 5.1 mmHg and 4.6 mmHg mean absolute error for SBP and DBP and 6.2 mmHg and 5.4 mmHg standard deviation for SBP and DBP. Moreover, the results of the individual models are significantly better than the generalized model built with the data of all subjects.

Estimation and Tracking of Blood Pressure Using Routinely Acquired Photoplethysmographic Signals and Deep Neural Networks

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Continuous tracking of blood pressure in critically ill patients allows rapid identification of clinically important changes and helps guide treatment. Classically, such tracking requires invasive monitoring with its associated risks, discomfort, and low availability outside critical care units. We hypothesized that information contained in a prevalent noninvasively acquired signal (photoplethysmograph: a byproduct of pulse oximetry) combined with advanced machine learning will allow continuous estimation of the patient’s blood pressure.