Conducting an analysis of human behavior is an intriguing topic for many researchers. Within this field, machine learning can be applied to classify activities and emotions by analyzing physiological signals. However, the limited size of available databases poses challenges for the generalization of classifiers. This paper proposes a method to enhance the generalization of neural network-based classifiers by intelligently initializing weights for emotion and activity recognition. The signals under consideration are electrocardiogram, thoracic electrical bioimpedance, and electrodermal activity. The database used comprises recordings from 40 subjects performing various tasks that induce emotions and activities. The performance of the proposed method is compared with several standard machine learning and deep learning classifiers typically employed in emotion and activity recognition. This study involves two primary assessments. First is the activity recognition task, encompassing classes such as neutral, emotional, mental, and physical activity, where results close to 20% accuracy are achieved using the three physiological signals. Second, the emotion recognition assessment aims to differentiate between emotions like neutral, sadness, and disgust. An error probability close to 15% is obtained using thoracic electrical bioimpedance and electrodermal activity. The proposed method yields the best results among the approaches evaluated.

Development in the field of smart wearable products for monitoring daily life health status is beginning to spread in society. Textile electronic methods are improving and facilitating the manufacturing of sensorized garments. This paper evaluates a newly developed t-shirt incorporating electronic sensing and interconnecting elements integrated into the T-shirt with textile-friendly techniques sensorized with a Movesense device for monitoring ECG and HR and activity. The measurement results obtained from the t-shirt are entirely in agreement with the measurements obtained with other textile garments and encourage us for a near future where wearable sensors are just textile garments sensorized seamlessly without suboptimal textile-electronic integrated elements. 

Previous studies have suggested an association between Proton Pump Inhibitors (PPIs) and the progression of chronic kidney disease (CKD). This study aims to assess the association between PPI use and CKD progression by analysing estimated glomerular filtration rate (eGFR) trajectories using a process mining approach. We conducted a retrospective cohort study from 1 January 2006 to 31 December 2011, utilising data from the Stockholm Creatinine Measurements (SCREAM). New users of PPIs and H2 blockers (H2Bs) with CKD (eGFR < 60) were identified using a new-user and active-comparator design. Process mining discovery is a technique that discovers patterns and sequences in events over time, making it suitable for studying longitudinal eGFR trajectories. We used this technique to construct eGFR trajectory models for both PPI and H2B users. Our analysis indicated that PPI users exhibited more complex and rapidly declining eGFR trajectories compared to H2B users, with a 75% increased risk (adjusted hazard ratio [HR] 1.75, 95% confidence interval [CI] 1.49 to 2.06) of transitioning from moderate eGFR stage (G3) to more severe stages (G4 or G5). These findings suggest that PPI use is associated with an increased risk of CKD progression, demonstrating the utility of process mining for longitudinal analysis in epidemiology, leading to an improved understanding of disease progression.

Process mining holds promise for analysing longitudinal data in clinical epidemiology, yet its application remains limited. The objective of this study was to propose and evaluate a methodology for applying process mining techniques in observational clinical epidemiology. We propose a methodology that integrates a cohort study design with data-driven process mining, with an eight-step approach, including data collection, data extraction and curation, event-log generation, process discovery, process abstraction, hypothesis generation, statistical testing, and prediction. These steps facilitate the discovery of disease progression patterns. We implemented our proposed methodology in a cohort study comparing new users of proton pump inhibitors (PPI) and H2 blockers (H2B). PPI usage was associated with a higher risk of disease progression compared to H2B usage, including a greater than 30% decline in estimated Glomerular Filtration Rate (eGFR) (Hazard Ratio [HR] 1.6, 95% Confidence Interval [CI] 1.4–1.8), as well as increased all-cause mortality (HR 3.0, 95% CI 2.1–4.4). Furthermore, we investigated the associations between each transition and covariates such as age, gender, and comorbidities, offering deeper insights into disease progression dynamics. Additionally, a risk prediction tool was developed to estimate the transition probability for an individual at a future time. The proposed methodology bridges the gap between process mining and epidemiological studies, providing a useful approach to investigating disease progression and risk factors. The synergy between these fields enhances the depth of study findings and fosters the discovery of new insights and ideas. 

In recent years the use of bioimpedance analysis (BIA) for assessment of fluid status as well as body composition as a mean to assess nutritional status in CKD has increased. The interest in the method is due to the associations between fluid overload and cardiovascular disease, and between fluid overload and malnutrition, both of which contribute to an increased risk of morbidity and mortality (Hur et al., 2013; Onofriescu et al., 2014). Moreover, BIA devices are suitable for clinical use, since they are portable, easy to use and, with a median to low price. However, the results can be difficult to interpret and integrate into routine clinical care, and although impedance measurements can contribute to an increased understanding of the patient's fluid balance, the results should be used with caution and in combination with other physiological parameters and clinical assessments (de Ruiter et al., 2020; Scotland et al., 2018). The aim of this editorial is to contribute to increased awareness of the benefits and limitations of using bioimpedance in patients with CKD with or without dialysis, and contribute to improving the measurement quality, facilitating interpretations, and highlighting possible sources of error.

Bioimpedance modeling with equivalent electrical circuits has an important role in various biomedical applications, as it facilitates understanding of underlying physical and electrochemical processes in applications such as body composition measurements and assessment of clinical conditions. However, the estimation of model parameter values is not a straightforward task, especially when complex circuits with fractional-order components [e.g., constant phase elements (CPEs)] are used. In this article, we propose a low-complexity method for parameter estimation of the Cole-impedance model suitable for low-cost embedded hardware (e.g., 8-bit microcontrollers). Our approach uses only the measured real and imaginary impedance, without any specific software package/toolbox, or initial values provided by the user. The proposed method was validated with synthetic (noiseless and noisy) data and experimental right-side, hand-to-foot bioimpedance data from a healthy adult participant. Moreover, the proposed method was compared in terms of accuracy with the recently published relevant work and commercial Electrical Impedance Spectroscopy software (Bioimp 2.3.4). The performance evaluation in terms of complexity (suitable for deployment for the microcontroller-based platform with 256 kB of RAM and 16 MHz clock speed), execution time (18 s for the dataset with 256 points), and cost (< 25) confirms the proposed method in regards to reliable bioimpedance processing using embedded hardware. 

The widespread use of information technology in healthcare leads to extensive data collection, which can be utilised to enhance patient care and manage chronic illnesses. Our objective is to summarise previous studies that have used data mining or process mining methods in the context of chronic diseases in order to identify research trends and future opportunities. The review covers articles that pertain to the application of data mining or process mining methods on chronic diseases that were published between 2000 and 2022. Articles were sourced from PubMed, Web of Science, EMBASE, and Google Scholar based on predetermined inclusion and exclusion criteria. A total of 71 articles met the inclusion criteria and were included in the review. Based on the literature review results, we detected a growing trend in the application of data mining methods in diabetes research.

Additionally, a distinct increase in the use of process mining methods to model clinical pathways in cancer research was observed. Frequently, this takes the form of a collaborative integration of process mining, data mining, and traditional statistical methods. In light of this collaborative approach, the meticulous selection of statistical methods based on their underlying assumptions is essential when integrating these traditional methods with process mining and data mining methods. Another notable challenge is the lack of standardised guidelines for reporting process mining studies in the medical field. Furthermore, there is a pressing need to enhance the clinical interpretation of data mining and process mining results.

Electro-stimulation to alleviate spasticity, pain and to increase mobility has been used successfully for years. Usually, gelled electrodes are used for this. In a garment intended for repeated use such electrodes must be replaced. The Mollii-suit by the company Inerventions utilises dry conductive rubber electrodes. The electrodes work satisfactory, but the garment is cumbersome to fit on the body. In this paper we show that knitted dry electrodes can be used instead. The knitted electrodes present a lower friction against the skin and a garment is easily fitted to the body. The fabric is stretchable and provides a tight fit to the body ensuring electrical contact. We present three candidate textrodes and show how we choose the one with most favourable features for producing the garment. We validate the performance of the garment by measuring three electrical parameters: rise time (10–90%) of the applied voltage, net injected charge and the low frequency value of the skin–electrode impedance. It is concluded that the use of flat knitting intarsia technique can produce a garment with seamlessly integrated conductive leads and electrodes and that this garment delivers energy to the body as targeted and is beneficial from manufacturing and comfort perspectives.