MoniCardi, a medical technology and software company originating from Tampere University, has been diligently developing novel and heart rate variability (HRV) methods to decode the intricate phenomena of the human body. The MoniCardi team aims to unveil the various physiological characteristics influenced by the heart's behavior, opening new frontiers in health and performance measurement.
The Foundation: Validation with Massive Datasets
MoniCardi's groundbreaking research is rooted in statistical and time-series analysis methods originally developed in computational physics. These methods have surprising, yet highly impactful applications in electrocardiography, including HRV analysis.
MoniCardi's novel methods and their usefulness have been validated in various scientific studies [1-9] and they have been featured in the leading conferences of cardiology such as the Scientific Sessions of American Heart Association. The studies include exploitation of massive datasets such as the extensive Finnish Cardiovascular Study (FINCAVAS), which contains comprehensive measurement data from 4386 participants of a clinical stress test. In a recent breakthrough study [1], it was found that MoniCardi's HRV analysis of a one-minute rest phase prior to the test predicts sudden cardiac death significantly better than the conventional analysis of the complete 20-minute stress test (hazard ratios of ~2.5 and ~1.5, respectively). The superiority of MoniCardi increases further when considering all the other risk factors in the analysis.
Outside clinical studies, MoniCardi's patented methodology allows accurate estimation of metabolic thresholds in sport applications. This was confirmed in a ground-breaking study published by the team in the leading physiological journal in 2023 [2]. The study was featured in several national and international news sites, including a full-page article in the main Finnish newspaper Helsingin Sanomat (link below). The results are currently under validation in academic collaboration between Tampere University and the Finnish Institute of High-Performance Sport (KIHU).
Through a partnership with Suunto launched in 2024, MoniCardi's novel technology is now entering in the use of professional athletes, sport enthusiasts and all the consumers interested in these novel features that push the HRV analysis into a completely new level and ensure practical and actionable results.
Understanding Heart Rate Variability (HRV)
Heart rate variability (HRV) measures the variation in the time intervals between consecutive heartbeats. By analyzing the fluctuations in these intervals, it is possible to gain insights into the body's state, particularly the autonomic nervous system's influence on the heart. Conventionally, HRV has been used to gauge recovery states during sleep through RMSSD (Root Mean Square of Successive Differences), which observes nightly changes in HRV to detect stress levels. At rest, the body shows significant variability between heartbeats, known as HRV. However, as the body encounters increased stress, the autonomic nervous system shifts into the fight-or-flight mode, resulting in minimal heart rate variability. This reduction in HRV can be used as an indicator to assess stress levels.
Introducing DDFA: A Revolutionary Measurement Technology
HRV methods are conventionally split into time-domain, frequency-domain and nonlinear methods. One of the most common nonlinear methods is Detrended Fluctuation Analysis (DFA) developed in the early 1990s. The key information provided by DFA is the overall long-term characteristics of HRV in terms of correlations, in particular, how changes in heartbeat intervals at some time affect the changes at another time. This information has powerful predictive value, but the practical usefulness of this information was unleashed only recently, when Dynamical DFA (DDFA) was developed [8,9] and further refined to assess changes in HRV correlations in a time-sensitive manner [10]. In brief, DDFA utilizes a multitude of "measure sticks" from 4 up to >50 consecutive heartbeats. At every instant of time, DDFA then gives a so-called scaling exponent - a characteristic feature of correlations in heartbeat intervals - for all these measure sticks simultaneously. This information can be precisely mapped to the physiological state during physical exercise.
Real-Time Intensity Monitoring
DDFA excels in assessing real-time changes in the heartbeat correlations during exercise. Training intensity directly correlates with time- and scale-dependent variations in the DDFA scaling exponent. Research indicates that increasing intensity in physical exercise decreases the scaling exponents. Eventually, at very high intensities, the beat-to-beat intervals may show so-called anticorrelations, where large and small beat-to-beat intervals alternate in a specific manner depending on the time scale. This information allows for precise monitoring of exercise intensity and physiological thresholds.
Visualizing DDFA in Action
A pivotal study, "Estimation of Physiological Exercise Thresholds Based on Dynamical Correlation Properties of Heart Rate Variability," published in Frontiers in Physiology in 2023 [2] illustrates DDFA's capabilities. The research paper presents an exercise scenario where intensity increases over time. The cyan lines denote the two metabolic thresholds: LT1 (aerobic threshold) and LT2 (anaerobic threshold), with the black dotted set on locations where these thresholds would be based blood lactate levels.
This illustrates an ideal scenario where the DDFA-based analysis yields threshold levels nearly identical to those obtained using lactate-based threshold definitions. While this represents the optimal case, variations are expected in real applications. The DDFA analysis and lactate-based thresholds may differ from case to case, with heart rate measurements typically matching within +-5 beats per minute. There are also uncertainties inherent in lactate thresholds, which are subject to interpretation.
Validity up to clinical accuracy
MoniCardi methodology has been used to predict the overall cardiac risk and sudden cardiac death [1], and several cardiac diseases such as long QT syndrome [4,5], atrial fibrillation and congestive heart failure [in preparation]. The methods have also been applied to estimate stress and sleep stages [6,7]. The prediction of sudden cardiac death [1] has gained traction and it was featured in all the big news sites in Finland (YLE, Helsingin Sanomat, Ilta-Sanomat, Aamulehti) and on several international news sites (list below).
In medical technology, MoniCardi is currently collaborating with Cardiolex Medical, a Swedish MedTech company developing modern ECG devices and systems. MoniCardi is also seeking partners in wearable technologies to bring cardiac risk assessment to mass markets.
References:
[1] Jussi Hernesniemi, Teemu Pukkila, Matti Molkkari, Kjell Nikus, Leo-Pekka Lyytikäinen, Terho Lehtimäki, Jari Viik, Mika Kähönen, Esa Räsänen, Prediction of sudden cardiac death with ultra-short-term heart rate fluctuations, JACC: Clinical Electrophysiology, 2024
[2] Matias Kanniainen, Teemu Pukkila, Joonas Kuisma, Matti Molkkari, Kimmo Lajunen, and Esa Räsänen, Estimation of Physiological Exercise Thresholds Based on Dynamical Correlation Properties of Heart Rate Variability, Front. Physiol. 14 (2023).
[3] Teemu Pukkila, Matti Molkkari, Matias Kanniainen, Jussi Hernesniemi, Kjell Nikus, Leo- Pekka Lyytikäinen, Terho Lehtimäki, Jari Viik, Mika Kähönen, and Esa Räsänen, Effects of Beta Blocker Therapy on RR Interval Correlations During Exercise, Computing in Cardiology 50 (2023) 10.22489/CinC.2023.104
[4] Matias Kanniainen, Teemu Pukkila, Matti Molkkari, and Esa Räsänen, Effect of Diurnal Rhythm on RR Interval Correlations of Long QT Syndrome, Computing in Cardiology 50 (2023) 10.22489/CinC.2023.287
[5] T. Pukkila, M. Molkkari, J. Kim, and E. Räsänen, Reduced RR Interval Correlations of Long QT Syndrome Patients, Computing in Cardiology 49 (2022) 10.22489/CinC.2022.284
[6] Teemu Pukkila, Matti Molkkari and Esa Räsänen, Dynamical Heartbeat Correlations During Complex Tasks – A Case Study in Automobile Driving, Computing in Cardiology 48 (2021) 10.23919/CinC53138.2021.9662676
[7] M. Molkkari, M. Tenhunen, A. Tarniceriu, A. Vehkaoja, S.-L. Himanen, and E. Räsänen,
Non-Linear Heart Rate Variability Measures in Sleep Stage Analysis with Photoplethysmography, Computing in Cardiology 46 (2019); 10.22489/cinc.2019.287
[8] M. Molkkari, G. Angelotti, T. Emig, and E. Räsänen, Dynamical Heartbeat Correlations During Running, Sci. Rep. 10, 13627 (2020)
[9] M. Molkkari and E. Räsänen, Robust Estimation of the Scaling Exponent in Detrended Fluctuation Analysis of Beat Rate Variability, Computing in Cardiology 45 (2018); 10.22489/CinC.2018.219
[10] M. Molkkari and E. Räsänen, Inter-beat interval of heart for estimating condition of subject, Patent pending.
Latest news of MoniCardi
International news:
Science Daily: https://www.sciencedaily.com/releases/2024/06/240613140808.htm
Science Alert: https://www.sciencealert.com/new-algorithm-can-predict-and-help- prevent-sudden-cardiac-death
Mirage News: https://www.miragenews.com/tampere-university-researchers- predict-sudden-1255528/
Medical XPress News: https://medicalxpress.com/news/2024-01-method-based-series-analysis-thresholds.html
Finnish news:
YLE: https://yle.fi/a/74-20093771
Helsingin Sanomat: https://www.hs.fi/tiede/art-2000009847625.html
Ilta-Sanomat: https://www.is.fi/terveys/art-2000010505400.html
Aamulehti: https://www.aamulehti.fi/tiedejateknologia/art-2000010497986.html https://www.aamulehti.fi/tiedejateknologia/art-2000009863997.html
STT: https://www.sttinfo.fi/tiedote/70082024/aikasarja-analyysiin-perustuva-uusi-menetelma-helpottaa-urheilun-kynnysarvojen- maarittamista?publisherId=69818730&lang=fi
