Implantable Fiber Bragg Grating Sensor for Continuous Heart Activity Monitoring: Ex-Vivo and In-Vivo Validation

被引:11
|
作者
Ferraro, Davide [1 ,2 ]
D'Alesio, Giacomo [1 ,2 ]
Camboni, Domenico [1 ,2 ]
Zinno, Ciro [1 ,2 ,3 ]
Costi, Leone [1 ,2 ,3 ]
Haberbusch, Max [4 ,5 ]
Aigner, Philipp [4 ,5 ]
Maw, Martin [4 ,5 ]
Schloglhofer, Thomas [4 ,5 ]
Unger, Ewald [4 ]
Aliperta, Andrea [1 ,2 ]
Bernini, Fabio [6 ]
Casieri, Valentina [6 ]
Terlizzi, Domiziana [7 ]
Giudetti, Guido [1 ,2 ]
Carpaneto, Jacopo [1 ,2 ]
Pedrizzetti, Gianni [8 ]
Micera, Silvestro [1 ,2 ,9 ]
Lionetti, Vincenzo [6 ,7 ,10 ]
Moscato, Francesco [4 ,5 ,11 ]
Massari, Luca [1 ,2 ]
Oddo, Calogero Maria [1 ,2 ]
机构
[1] Scuola Super Sant Anna, BioRobot Inst, I-56025 Pisa, Italy
[2] Scuola Super Sant Anna, Dept Excellence Robot & AI, I-56127 Pisa, Italy
[3] Univ Pisa, Dept Informat Engn, I-56122 Pisa, Italy
[4] Med Univ Vienna, Ctr Med Phys & Biomed Engn, A-1090 Vienna, Austria
[5] Ludwig Boltzmann Inst Cardiovasc Res, A-1090 Vienna, Austria
[6] Scuola Super Sant Anna, Inst Life Sci, I-56127 Pisa, Italy
[7] UOS Anesthesiol & Intens Care Med, Fdn Toscana Gabriele Monasterio Ric Med & Sanita, I-56124 Pisa, Italy
[8] Univ Trieste, Dept Engn & Architecture, I-34127 Trieste, Italy
[9] Ecole Polytech Fed Lausanne, Translat Neural Engn Lab, Ctr Neuroprosthet & Inst Bioengn, CH-1015 Lausanne, Switzerland
[10] UOS Anesthesiol & Intens Care Med, Ric Med & Sanita Pubbl, I-56124 Pisa, Italy
[11] Austrian Cluster Tissue Regenerat, A-1200 Vienna, Austria
关键词
Sensors; Heart; Strain; Monitoring; Optical fiber sensors; Fiber gratings; Mechanical sensors; Fiber Bragg grating; heart monitoring; neural networks; RATE-VARIABILITY; PRESSURE; INCREASE; DOPPLER;
D O I
10.1109/JSEN.2021.3056530
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Continuous and reliable cardiac function monitoring could improve medication adherence in patients at risk of heart failure. This work presents an innovative implantable Fiber Bragg Grating-based soft sensor designed to sense mechanical cardiac activity. The sensor was tested in an isolated beating ovine heart platform, with 3 different hearts operated in wide-ranging conditions. In order to investigate the sensor capability to track the ventricular beats in real-time, two causal algorithms were proposed for detecting the beats from sensor data and to discriminate artifacts. The first based on dynamic thresholds while the second is a hybrid convolutional and recurrent Neural Network. An error of 2.7 +/- 0.7 beats per minute was achieved in tracking the heart rate. Finally, we have confirmed the sensor reliability in monitoring the heart activity of healthy adult minipig with an error systematically lower than 1 Bpm.
引用
收藏
页码:14051 / 14059
页数:9
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