| 11. |
- Holmer, Mattias, et al.
(författare)
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Determining Heart Activity Present in the Pressure Sensors of a Dialysis Machine
- 2013
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Ingår i: Computing in Cardiology. - 2325-8861. ; , s. 217-220
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Konferensbidrag (refereegranskat)abstract
- Determination of heart status during dialysis can im- prove patient monitoring. Pressure sensors in the dialysis machine measures the heart pulses that propagates in the body and enter the extracorporeal blood circuit. A peri- staltic blood pump, located in the same circuit, introduces strong periodic pressure pulses that interfere with the much weaker cardiac component. These signal characteristics make the extraction of the heart activity challenging. In the present study, we explore the possibility to extract and analyze the cardiac component using simulated data. The accuracy of the timing of each heartbeat is analyzed. Ad- ditionally, the heart component is extracted from patient pressure recordings, and compared to the heart rate com- puted from a photoplethysmogram. The results show that heart timings can be accurately determined using the pres- sure sensors of a dialysis machine.
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| 12. |
- Holmer, Magnus, et al.
(författare)
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Effect of common genetic variants on the risk of cirrhosis in non-alcoholic fatty liver disease during 20 years of follow-up
- 2022
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Ingår i: Liver international (Print). - : Wiley. - 1478-3223 .- 1478-3231. ; 42:12, s. 2769-2780
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Tidskriftsartikel (refereegranskat)abstract
- Background and Aims Several genotypes associate with a worse histopathological profile in patients with non-alcoholic fatty liver disease (NAFLD). Whether genotypes impact long-term outcomes is unclear. We investigated the importance of PNPLA3, TM6SF2, MBOAT7 and GCKR genotype for the development of severe outcomes in NAFLD. Method DNA samples were collected from 546 patients with NAFLD. Advanced fibrosis was diagnosed by liver biopsy or elastography. Non-alcoholic steatohepatitis (NASH) was histologically defined. Additionally, 5396 controls matched for age, sex and municipality were identified from population-based registers. Events of severe liver disease and all-cause mortality were collected from national registries. Hazard ratios (HRs) adjusted for age, sex, body mass index and type 2 diabetes were estimated with Cox regression. Results In NAFLD, the G/G genotype of PNPLA3 was associated with a higher prevalence of NASH at baseline (odds ratio [OR] 3.67, 95% CI = 1.66-8.08), but not with advanced fibrosis (OR 1.81, 95% CI = 0.79-4.14). After up to 40 years of follow-up, the PNPLA3 G/G genotype was associated with a higher rate of severe liver disease (adjusted hazard ratio [aHR] 2.27, 95% CI = 1.15-4.47) compared with the C/C variant. NAFLD patients developed cirrhosis at a higher rate than controls (aHR 9.00, 95% CI = 6.85-11.83). The PNPLA3 G/G genotype accentuated this rate (aHR 23.32, 95% = CI 9.14-59.47). Overall mortality was not affected by any genetic variant. Conclusion The PNPLA3 G/G genotype is associated with an increased rate of cirrhosis in NAFLD. Our results suggest that assessment of the PNPLA3 genotype is of clinical relevance in patients with NAFLD to individualize monitoring and therapeutic strategies.
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| 13. |
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| 14. |
- Holmer, Mattias, et al.
(författare)
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Extracting a Cardiac Signal From the Extracorporeal Pressure Sensors of a Hemodialysis Machine
- 2015
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Ingår i: IEEE Transactions on Biomedical Engineering. - 1558-2531. ; 62:5, s. 1305-1315
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Tidskriftsartikel (refereegranskat)abstract
- Although patients undergoing hemodialysis treatment often suffer from cardiovascular disease, monitoring of cardiac rhythm is not performed on a routine basis. Without requiring any extra sensor, this study proposes a method for extracting a cardiac signal from the built-in extracorporeal venous pressure sensor of the hemodialysis machine. The extraction is challenged by the fact that the cardiac component is much weaker than the pressure component caused by the peristaltic blood pump. To further complicate the extraction problem, the cardiac component is difficult to separate when the pump and heart rates coincide. The proposed method estimates a cardiac signal by subtracting an iteratively refined blood pump model signal from the signal measured at the extracorporeal venous pressure sensor. The method was developed based on simulated pressure signals, and evaluated on clinical pressure signals acquired during hemodialysis treatment. The heart rate estimated from the clinical pressure signal was compared to that derived from a photoplethysmographic reference signal, resulting in a difference of 0.07 +/- 0.84 beats/min. The accuracy of the heartbeat occurrence times was studied for different strengths of the cardiac component, using both clinical and simulated signals. The results suggest that the accuracy is sufficient for analysis of heart rate and certain arrhythmias.
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| 15. |
- Holmer, Mattias
(författare)
-
Extracting Cardiac Information From the Pressure Sensors of a Dialysis Machine
- 2017. - 1
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- This doctoral thesis in biomedical engineering deals with cardiac monitoring using the built-in extracorporeal blood pressure sensors of a hemodialysis machine. Patients treated with hemodialysis often suffer from cardiovascular disease. Despite this, cardiac monitoring is not routinely performed during dialysis treatment, since external devises required for such monitoring causes patient discomfort and increased workload for the clinical staff. Extraction of cardiac information from the pressure sensor signals is complicated by the fact that the cardiac pressure pulses are obscured by pressure pulses caused by the peristaltic blood pump of the dialysis machine. The cardiac signal component is of much lower magnitude than the pump pressure component, and the pump and heart rates may coincide.The thesis comprises an introduction and four papers describing methods for extraction and characterization of cardiac information from the built-in pressure sensors of a dialysis machine. In the first paper, a method is proposed for estimating the cardiac signal by subtracting an iteratively refined blood pump model signal from the signal measured at the extracorporeal venous pressure sensor. The method was developed based on simulated pressure signals, and evaluated on clinical pressure signals acquired during hemodialysis treatment. Heart rate estimated from the clinical pressure signal was compared to heart rate derived from a reference photoplethysmographic (PPG) signal. The results suggest that the accuracy is sufficient for analysis of heart rate and certain arrhythmias. In the second paper, a method is proposed for improved cardiac signal extraction by combining the arterial and the venous pressure sensor signals of the hemodialysis machine. Using different techniques for combining the arterial and venous pressure signals, the performance is evaluated and compared to that of the method in the first paper. Heart rate and heartbeat occurrence times, estimated from the extracted cardiac signal, are compared to the corresponding quantities estimated from the PPG reference signal in nine hemodialysis treatments. The results show that the proposed method offers superior estimation at low cardiac signal amplitudes, enabling cardiac monitoring during treatment without the need of extra sensors for more patients. Ventricular premature beats (VPBs), being abundant in hemodialysis patients, can provide information on cardiovascular instability and electrolyte imbalance. The third paper describes a method for VPB detection in cardiac signals extracted using the method described in the second paper. A set of features characterizing the cardiac pressure pulses is extracted, and linear discriminant analysis is performed to classify beats as normal or VPB. Performance is evaluated on signals from nine hemodialysis treatments, using leave-one-out cross validation. The simultaneously recorded and annotated PPG signal serves as reference. The results show that VPBs can be reliably detected for average cardiac pulse pressures exceeding 1 mmHg. In the fourth paper, we propose a novel method for detection of venous needle dislodgement. Four features, extracted from the arterial and venous pressure sensors signals, are used as input to a support vector machine which determine whether the venous needle is dislodged. The support vector machine is trained on a set of laboratory data, and tested on an-other set of laboratory data as well as on four clinical recordings. The results show that dislodgement is detected long before the blood loss has caused serious injury to the patient. In summary, the work in this thesis contributes with novel methods for extraction of cardiac information using the built-in pressure sensors of the dialysis machine. The main benefit of such approach for cardiac monitoring, compared to existing techniques, is that no extra sensors are required. All results are preliminary, and the methods needs to be validated on a larger set of clinical recordings.
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| 16. |
- Holmer, Mattias, et al.
(författare)
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Heart Rate Estimation from Dual Pressure Sensors of a Dialysis Machine
- 2015
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Ingår i: 2015 Computing in Cardiology Conference (CinC). - 2325-8861. ; 42, s. 29-32
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Konferensbidrag (refereegranskat)abstract
- Dialysis patients often suffer from cardiovascular dis- eases, motivating the use of continuous monitoring of car- diac activity in clinical routine. Cardiac pressure pulses propagate through the vascular system and enter the ex- tracorporeal blood circuit of a dialysis machine, where the pulses are captured by pressure sensors. The cardiac pulses are obscured by the much stronger pressure pulses originating from the peristaltic blood pump. We have pre- viously shown that a cardiac signal can be extracted from the venous pressure signal. However, that method has been found to perform less well at very low cardiac pressure pulse amplitudes. In the present study, we propose a novel method which addresses this issue by using the signals from both the arterial and the venous pressure sensors. The method is compared to the previous method on clini- cal data using a photoplethysmogram as reference. The re- sults suggests that heart rate can be estimated more accu- rately from pressure signals with lower cardiac signal am- plitude when both arterial and venous pressure are used, compared to when only the venous signal is used.
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| 17. |
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| 18. |
- Holmer, Mattias, et al.
(författare)
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On-line Heart Rate Monitoring Using the Extra-corporeal Pressure Sensors of a Dialysis Machine
- 2012
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Ingår i: Biomedical engineering – 2012 : proceedings of international conference. - 2029-3380.
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Konferensbidrag (refereegranskat)abstract
- Heart rate can be extracted from the extracorporeal venous pressure signal of a dialysis machine. The results are of comparable accuracy and reliability to the ones obtained by the PPG reference signal from a pulse oximeter. Difficulties that occur during heart rate estimation were determined, and some can be overcome by a slight adjustment of blood flow rate. The described techniques, after being implemented into the dialysis machines, would help to improve current hemodialysis safety throughout the treatment by on-line monitoring of the cardiac activity.
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| 19. |
- Holmer, Olov, et al.
(författare)
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Energy-Based Survival Models for Predictive Maintenance
- 2023
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Ingår i: IFAC PAPERSONLINE. - : ELSEVIER. ; , s. 10862-10867
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Konferensbidrag (refereegranskat)abstract
- Predictive maintenance is an effective tool for reducing maintenance costs. Its effectiveness relies heavily on the ability to predict the future state of health of the system, and for this survival models have shown to be very useful. Due to the complex behavior of system degradation, data-driven methods are often preferred, and neural network-based methods have been shown to perform particularly very well. Many neural network- based methods have been proposed and successfully applied to many problems. However, most models rely on assumptions that often are quite restrictive and there is an interest to find more expressive models. Energy-based models are promising candidates for this due to their successful use in other applications, which include natural language processing and computer vision. The focus of this work is therefore to investigate how energy-based models can be used for survival modeling and predictive maintenance. A key step in using energy- based models for survival modeling is the introduction of right-censored data, which, based on a maximum likelihood approach, is shown to be a straightforward process. Another important part of the model is the evaluation of the integral used to normalize the modeled probability density function, and it is shown how this can be done efficiently. The energy-based survival model is evaluated using both simulated data and experimental data in the form of starter battery failures from a fleet of vehicles, and its performance is found to be highly competitive compared to existing models. Code available at https://github.com/oholmer/PySaRe. Copyright (c) 2023 The Authors.
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| 20. |
- Olde, Bo
(creator_code:cre_t)
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Filtering of a time-dependent pressure signal
- 2015
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Patent (övrigt vetenskapligt/konstnärligt)abstract
- A device removes first pulses in a pressure signal of a pressure sensor which is arranged in a fluid containing system to detect the first pulses, which originate from a first pulse generator, and second pulses, which originate from a second pulse generator. The first pulse generator is known to operate in a sequence of pulse cycles, each pulse cycle resulting in at least one first pulse. The device repetitively obtains a current data sample, calculates corresponding a reference value and subtracts the reference value from the current data sample. The reference value is calculated as a function of other data sample(s) in the same pressure signal.; These data sample(s) may be either cycle-synchronized so as to have a corresponding location in one or more other pulse cycles (e.g. in a preceding pulse cycle) as the current data sample, or be located in proximity to the current data sample. The fluid containing system may include an extracorporeal blood flow circuit, e.g. as part of a dialysis machine, and a cardiovascular system of a human patient.
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