Monitoring phrenic nerve stimulation-induced breathing via tracheal sounds

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HAL Id: lirmm-02304843

https://hal-lirmm.ccsd.cnrs.fr/lirmm-02304843

Submitted on 3 Oct 2019

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Monitoring phrenic nerve stimulation-induced breathing via tracheal sounds

Xinyue Lu, David Guiraud, Serge Renaux, Thomas Similowski, Christine Azevedo Coste

To cite this version:

Xinyue Lu, David Guiraud, Serge Renaux, Thomas Similowski, Christine Azevedo Coste. Monitoring phrenic nerve stimulation-induced breathing via tracheal sounds. IFESS 2019 - RehabWeek 2019, Jun 2019, Toronto, Canada. �lirmm-02304843�

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Xinyue LU

1, 2

, David GUIRAUD

1 , Serge RENAUX

2 , Thomas SIMILOWSKI

3, 4

,

Christine AZEVEDO

1

1 _{INRIA, University of Montpellier, Montpellier, France}

2 _{NeuroResp, Les Aires, France}

3 _{INSERM, University of Sorbonne, UMRS1158, Paris, France}

4 _{APHP, Paris, France}

Monitoring phrenic nerve stimulation-induced breathing via tracheal sounds

Central respiratory paralysis induces a dependence on artificial ventilation If

patient’s phrenic nerves and diaphragm remain functional, diaphragm pacing (DP) through electrical stimulation can provide a more natural respiration instead of

mechanical ventilation [1]. However, commercialized systems do not embed any respiratory monitoring function and cannot adapt to patients’ electro-ventilation

needs. To increase the performance and safety of these systems, in this study, a

real-time acoustic respiratory monitoring method based on a microphone is

investigated. This method is tested on recordings from an individual equipped with a commercial intrathoracic phrenic nerve stimulation (PNS) system: AtroStim®, Atrotech (Fig.1).

Figure 1 – Structure of AtroStim® PNS system [2]

References:

1. F. Le Pimpec-Barthes et al., “Diaphragm pacing: The state of the art,” J. Thorac. Dis., vol. 8, no. Suppl 4, pp. S376–S386, 2016.The first obtained result is very promising;

2. F. Le Pimpec-Barthes et al., “Intrathoracic phrenic pacing: A 10-year experience in France,” J. Thorac. Cardiovasc. Surg., vol. 142, no. 2, pp. 378–383, 2011.

Materials:

 An omni-directional microphone was inserted into a 3D-printed support, which was positioned above patient’s tracheotomy (Fig.2.)

 Tracheal sounds were first filtered (100Hz - 1200Hz) and amplified (230 times), then sampled at 8500Hz.

Signals analysis:

One patient with high spinal cord injury and under stimulation participated in this observational study. One recording of 30 seconds was shown in Fig.3, and one enlarged cycle is shown in Fig.4:

 Noises are circled in green;

 Induced inspiration, expiration and captured stimulation image are indicated in orange;

 Some pre-inspirations are circled in red, one enlarged example is presented in Fig.4.b, in which two inspirations were induced during one cycle of stimulation.

Figure 2 – Position of microphone

Figure 3 – Recording under stimulation

Detection algorithm:

Tracheal sounds recordings are processed in real-time with a delay of 0.22s, corresponds to a moving segment of 3×1024 samples. As shown in the detection flow diagram (Fig.5), the segment of recording is first high-pass filtered at 300 Hz to remove cardiac noises, then processed both in temporal and frequency domains. At the end, the detection results of theses two domains are combined to get the final result.

Contact : xinyue.lu@inria.fr

Figure 4 – One enlarged cycle of recording under stimulation

Poster number: IFESS n°22

For activing muscle fibers progressively to avoid a strong contraction, stimulation intensity increases from I_threshold to I_TidalVolume, and then the induced inspiration begins (Fig.4.a). A pre-inspiration appears during increasing phase (Fig.4.b) may because a too high I_thresould had been set.

Figure 5 – Detection flow diagram

Recording

Respiration

Envelope

HP 300Hz

𝑃𝑆𝐷

₃₀₀600 > 0.4 s Threshold Temporal detection result Minimum threshold (T_min) Frequency detection result PSD > T_adap > T_min Adaptive threshold (T_min) < 1s Final detection result Temporal detection frequency detection

The detection result of this 30 seconds recording is illustrated in Fig.6:

 All 9 induced inspirations and 8 expirations are detected;  Noises at 2s, 6s, 17s and 21s (in Fig.3) are eliminated;

 All pre-inspirations are not taken into account.

Discussion:

 Proposed respiratory detection method for DP monitoring allows detecting

breathing events and electrical stimulation;

 Short noises can be eliminated by temporal detection, while long noises similar to respiration can be eliminated by frequency detection;

 Synchronization of the respiration detection with captured stimulation image can

indicate a bad/lost electrodes' contact, obstructive apnea, …