98 citations · 338 across the 9 of their papers we have counts for
10 papers
Anomalous Change Point Detection Using Probabilistic Predictive Coding
Roelof G. Hup, Julian P. Merkofer, Alex A. Bhogal +3
Change point detection (CPD) and anomaly detection (AD) are essential techniques in various fields to identify abrupt changes or abnormal data instances. However, existing methods…
Robust probabilistic modeling of photoplethysmography signals with application to the classification of premature beats
M. Regis, L. M. Eerikäinen, R. Haakma +2
In this paper we propose a robust approach to model photoplethysmography (PPG) signals. After decomposing the signal into two components, we focus the analysis on the pulsatile par…
Estimating blood pressure trends and the nocturnal dip from photoplethysmograph
Mustafa Radha, Koen de Groot, Nikita Rajani +9
Objective: Evaluate a method for the estimation of the nocturnal systolic blood pressure (SBP) dip from 24-hour blood pressure trends using a wrist-worn photoplethysmography (PPG)…
Mining Process Model Descriptions of Daily Life through Event Abstraction
Niek Tax, Natalia Sidorova, Reinder Haakma +1
Process mining techniques focus on extracting insight in processes from event logs. Process mining has the potential to provide valuable insights in (un)healthy habits and to contr…
Generating Time-Based Label Refinements to Discover More Precise Process Models
Niek Tax, Emin Alasgarov, Natalia Sidorova +2
Process mining is a research field focused on the analysis of event data with the aim of extracting insights related to dynamic behavior. Applying process mining techniques on data…
Heuristic Approaches for Generating Local Process Models through Log Projections
Niek Tax, Natalia Sidorova, Wil M. P. van der Aalst +1
Local Process Model (LPM) discovery is focused on the mining of a set of process models where each model describes the behavior represented in the event log only partially, i.e. su…
On Generation of Time-based Label Refinements
Niek Tax, Emin Alasgarov, Natalia Sidorova +1
Process mining is a research field focused on the analysis of event data with the aim of extracting insights in processes. Applying process mining techniques on data from smart hom…
Event Abstraction for Process Mining using Supervised Learning Techniques
Niek Tax, Natalia Sidorova, Reinder Haakma +1
Process mining techniques focus on extracting insight in processes from event logs. In many cases, events recorded in the event log are too fine-grained, causing process discovery…
Log-based Evaluation of Label Splits for Process Models
Niek Tax, Natalia Sidorova, Reinder Haakma +1
Process mining techniques aim to extract insights in processes from event logs. One of the challenges in process mining is identifying interesting and meaningful event labels that…
Mining Local Process Models
Niek Tax, Natalia Sidorova, Reinder Haakma +1
In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as \emph{local process models}. Local process model mining can…