Hidden Markov model detection of interpersonal interaction dynamics in predicting patient depression improvement in psychotherapy: Proof-of-concept study

Background Previous human ethology studies have demonstrated that the interpersonal interactions displayed in therapy by both patients and therapists influences a patient's depression improvement. Pairing novel statistical techniques such as the hidden Markov model (HMM), interpersonal interaction dynamics can be uncovered by partitioning time into empirically-derived nonverbal behavioral states. This approach allows for better patient-therapist behavioral dynamics distinctions in predicting depression improvement and, subsequently, for the processes behind depression improvement. Methods For the 39 participating patients, the first 15 min of the first or second therapy session was recorded on video to examine the interpersonal interaction behaviors of patients and therapists. The video recordings were encoded for vocalization, looking and leg movement behavior events at a 1 s frequency. A Bayesian multivariate multilevel HMM was fitted on the behavioral event data. Results It is demonstrated that patients that show improvement in the depression score are characterized by interpersonal interaction dynamics of hyperfocus when listening to their therapist in psychotherapy when compared to non-improving patients. The data supports evidence for the emergence of differences in interpersonal interaction dynamics through changed durations of the patient hyper focused listening states, but not through changed state-switching dynamics over time. Limitations Due to our relatively small sample size we could not fit multilevel HMMs composed of more than three hidden states. Conclusions We suggest that applying HMMs will aid human ethological behavior studies in uncovering interpersonal interaction dynamics that occur in therapy and be able to use these dynamics to predict patient depression symptom improvement

Go Multivariate: Recommendations on Bayesian Multilevel Hidden Markov Models with Categorical Data

The multilevel hidden Markov model (MHMM) is a promising method to investigate intense longitudinal data obtained within the social and behavioral sciences. The MHMM quantifies information on the latent dynamics of behavior over time. In addition, heterogeneity between individuals is accommodated with the inclusion of individual-specific random effects, facilitating the study of individual differences in dynamics. However, the performance of the MHMM has not been sufficiently explored. We performed an extensive simulation to assess the effect of the number of dependent variables (1–8), number of individuals (5–90), and number of observations per individual (100–1600) on the estimation performance of a Bayesian MHMM with categorical data including various levels of state distinctiveness and separation. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Moreover, including variables only consisting of random noise was generally not detrimental to model performance. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. However, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the level of state distinctiveness and separation and study objectives of the researcher

Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure

Aims Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD). Methods and results In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods. Conclusion The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement

Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex

Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analysing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity but also because of changes in the signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analysing such data in terms of discrete latent states, but previous approaches have not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modelled condition-specific differences. We present a multilevel Bayesian HMM addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping and placing task. We show that, in line with previous work, the model identifies latent neural population states which are tightly linked to behavioural events, despite the model being trained without any information about event timing. The association between these states and corresponding behaviour is consistent across multiple days of recording. Notably, this consistency is not observed in the case of a single-level HMM, which fails to generalise across distinct recording sessions. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations

Accurate differentiation between physiological and pathological ripples recorded with scalp-EEG

OBJECTIVE: To compare scalp-EEG recorded physiological ripples co-occurring with vertex waves to pathological ripples co-occurring with interictal epileptiform discharges (IEDs). METHODS: We marked ripples in sleep EEGs of children. We compared the start of ripples to vertex wave- or IED-start, and duration, frequency, and root mean square (RMS) amplitude of physiological and pathological ripples using multilevel modeling. Ripples were classified as physiological or pathological using linear discriminant analysis. RESULTS: We included 40 children with and without epilepsy. Ripples started (χ2(1) = 38.59, p < 0.001) later if they co-occurred with vertex waves (108.2 ms after vertex wave-start) than if they co-occurred with IEDs (4.3 ms after IED-start). Physiological ripples had longer durations (75.7 ms vs 53.0 ms), lower frequencies (98.3 Hz vs 130.6 Hz), and lower RMS amplitudes (0.9 μV vs 1.8 μV, all p < 0.001) than pathological ripples. Ripples could be classified as physiological or pathological with 98 % accuracy. Ripples recorded in children with idiopathic or symptomatic epilepsy seemed to form two subgroups of pathological ripples. CONCLUSIONS: Ripples co-occurring with vertex waves or IEDs have different characteristics and can be differentiated as physiological or pathological with high accuracy. SIGNIFICANCE: This is the first study that compares physiological and pathological ripples recorded with scalp EEG

Data-driven monitoring in patients on left ventricular assist device support

Introduction: Despite an increasing population of patients supported with a left ventricular assist device (LVAD), it remains a complex therapy, and patients are frequently admitted. Therefore, a strict follow-up including frequent hospital visits, patient self-management and telemonitoring is needed. Areas covered: The current review describes the principles of LVADs, the possibilities of (tele)monitoring using noninvasive and invasive devices. Furthermore, possibilities, challenges, and future perspectives in this emerging field are discussed. Expert Opinion: Several studies described initial experiences on telemonitoring in LVAD patients, using mobile phone applications to collect clinical data and pump data. This may replace frequent hospital visits in near future. In addition, algorithms were developed aiming to early detect pump thrombosis or driveline infections. Since not all complications are reflected by pump parameters, data from different sources should be combined to detect a broader spectrum of complications in an early stage. We need to focus on the development of sophisticated but understandable algorithms and infrastructure combining different data sources, while addressing essential aspects such as data safety, privacy, and cost-effectiveness

Monitoring left ventricular assist device parameters to detect flow- and power-impacting complications: a proof of concept

Aims The number of patients on left ventricular assist device (LVAD) support increases due to the growing number of patients with end-stage heart failure and the limited number of donor hearts. Despite improving survival rates, patients frequently suffer from adverse events such as cardiac arrhythmia and major bleeding. Telemonitoring is a potentially powerful tool to early detect deteriorations and may further improve outcome after LVAD implantation. Hence, we developed a personalized algorithm to remotely monitor HeartMate3 (HM3) pump parameters aiming to early detect unscheduled admissions due to cardiac arrhythmia or major bleeding. Methods and results The source code of the algorithm is published in an open repository. The algorithm was optimized and tested retrospectively using HeartMate 3 (HM3) power and flow data of 120 patients, including 29 admissions due to cardiac arrhythmia and 14 admissions due to major bleeding. Using a true alarm window of 14 days prior to the admission date, the algorithm detected 59 and 79% of unscheduled admissions due to cardiac arrhythmia and major bleeding, respectively, with a false alarm rate of 2%. Conclusion The proposed algorithm showed that the personalized algorithm is a viable approach to early identify cardiac arrhythmia and major bleeding by monitoring HM3 pump parameters. External validation is needed and integration with other clinical parameters could potentially improve the predictive value. In addition, the algorithm can be further enhanced using continuous data

Monitoring left ventricular assist device parameters to detect flow- and power-impacting complications: a proof of concept

AIMS: The number of patients on left ventricular assist device (LVAD) support increases due to the growing number of patients with end-stage heart failure and the limited number of donor hearts. Despite improving survival rates, patients frequently suffer from adverse events such as cardiac arrhythmia and major bleeding. Telemonitoring is a potentially powerful tool to early detect deteriorations and may further improve outcome after LVAD implantation. Hence, we developed a personalized algorithm to remotely monitor HeartMate3 (HM3) pump parameters aiming to early detect unscheduled admissions due to cardiac arrhythmia or major bleeding. METHODS AND RESULTS: The source code of the algorithm is published in an open repository. The algorithm was optimized and tested retrospectively using HeartMate 3 (HM3) power and flow data of 120 patients, including 29 admissions due to cardiac arrhythmia and 14 admissions due to major bleeding. Using a true alarm window of 14 days prior to the admission date, the algorithm detected 59 and 79% of unscheduled admissions due to cardiac arrhythmia and major bleeding, respectively, with a false alarm rate of 2%. CONCLUSION: The proposed algorithm showed that the personalized algorithm is a viable approach to early identify cardiac arrhythmia and major bleeding by monitoring HM3 pump parameters. External validation is needed and integration with other clinical parameters could potentially improve the predictive value. In addition, the algorithm can be further enhanced using continuous data

Developing a personalized remote patient monitoring algorithm: a proof-of-concept in heart failure

AIMS: Non-invasive remote patient monitoring is an increasingly popular technique to aid clinicians in the early detection of worsening heart failure (HF) alongside regular follow-ups. However, previous studies have shown mixed results in the performance of such systems. Therefore, we developed and evaluated a personalized monitoring algorithm aimed at increasing positive-predictive-value (PPV) (i.e. alarm quality) and compared performance with simple rule-of-thumb and moving average convergence-divergence algorithms (MACD). METHODS AND RESULTS: In this proof-of-concept study, the developed algorithm was applied to retrospective data of daily bodyweight, heart rate, and systolic blood pressure of 74 HF-patients with a median observation period of 327 days (IQR: 183 days), during which 31 patients experienced 64 clinical worsening HF episodes. The algorithm combined information on both the monitored patients and a group of stable HF patients, and is increasingly personalized over time, using linear mixed-effect modelling and statistical process control charts. Optimized on alarm quality, heart rate showed the highest PPV (Personalized: 92%, MACD: 2%, Rule-of-thumb: 7%) with an F1 score of (Personalized: 28%, MACD: 6%, Rule-of-thumb: 8%). Bodyweight demonstrated the lowest PPV (Personalized: 16%, MACD: 0%, Rule-of-thumb: 6%) and F1 score (Personalized: 10%, MACD: 3%, Rule-of-thumb: 7%) overall compared methods. CONCLUSION: The personalized algorithm with flexible patient-tailored thresholds led to higher PPV, and performance was more sensitive compared to common simple monitoring methods (rule-of-thumb and MACD). However, many episodes of worsening HF remained undetected. Heart rate and systolic blood pressure monitoring outperformed bodyweight in predicting worsening HF. The algorithm source code is publicly available for future validation and improvement