Statistical analysis

No correlation is found between the detected A- train times bu the model and the directly post- op HB, 6 weeks post-op HB, 6 months post-op HB and 1 year post-op HB. Furthermore, there is no correlation found between the detected A-train times and the ∆HB. The fact that the automatic detection script does not show a correlation with

the HB score either, yet shows correlation with the A-train times found by the model, suggests that total A-train time is not an accurate predictor for post-op facial nerve damage within our database of patients. This can be explained by the unknown origin of A-trains.

It is not fully understood how an A-train is formed when physical damage is inflicted to a nerve. The pattern of an A-train has a similar ap- pearance as neuromyotonic discharges in patients with generalized stiffness, hyperhidrosis and de- layed muscle relaxation after contraction.26 The pattern of neuromyotonic discharges is similar to the pattern of discharges seen in myotonia of mus- cle origin. Neuromyotonic discharges are associ- ated with repetitive discharges of a single motor unit with a frequency range of 150Hz to 250Hz. Whereas the discharges seen in myotonia of muscle origin is associated with spontaneous discharges of muscle fibers with a frequency range of 5Hz to 60Hz.26

Typical neuromyotonic discharges have a rapid decrementation in amplitude, whereas this is not often seen in A-trains. The constant amplitude and intermittent nature of the bursts is specific for an A-train. This could be exlpained by the fact that it is thought that neuromyotonic discharges are generated by peripheral motor axons, whereas the source of the discharges in an A-train can be considered to have a more neuronal source.

5.2 Literature comparison

Automatic A-train detection is previously assessed by Prell et al. In 2007 the A-train times calculated by the automatic detection algorithm have a mean of 16.77 seconds with a standard deviation (SD) of 36.49 seconds.17 The train times found in this study differ greatly from the train times in this study. The explanation of the difference between A-train times found in our study compared to the A-train times found by Prell et al. is explained by Prell et al. in 2014.7. The automatic detec- tion algorithm used by Prell et al. detects 1.46% of the total A-train time when compared to visual analysis of the data. The median train time mak- ing use of the automatic detection algorithm was 0.24 seconds with a SD of 6.61 seconds, whereas the median train time upon visual analysis was 19.41 seconds with a SD of 710.65 seconds. The

mean train time found in this study is 58.77 sec- onds with a SD of 177.44 seconds. And is therefore more comparable to the A-train times found upon visual analysis by Prell et al. than the automatic detection algorithm by Prell et al.

A significant Spearman’s correlation between the found A-train time and post-op HB score is found by Prell et al., whereas our study did not find a correlation. This can not be compared to ea- chother directly, since not the same databases are used. However, the databases both contain only patients that underwent monitored CPA tumor resection. The differences between the database of Prell et al. and our study is the tumour size. Prell et al. performs surgery on asymptomatic pa- tients with Koos grades lower than IV, whereas in our study only symptomatic patients with a tu- mour of predominantly a Koos grade of IV un- dergo surgery. 35 out of 79 patients of Prell et al. have a Koos grade lower than IV.7 _Potentially

resulting in a lower average A-train time due to the difference in pre-selection of patients.

All patients in this study are measured with two parallel electrodes per monitored facial mus- cle, resulting in one EMG channel per muscle. Prell et al. used 4 parallel electrodes per moni- tored facial muscle, resulting in three EMG chan- nels per muscle. In addition to this, the patients in this study are monitored in four muscles inner- vated by the facial nerve (m. orbicularis oculi, m. orbicularis oris, m. nasalis and m. mentalis). Prell et al. monitored 3 different muscles inner- vated by the facial nerve (m. orbicularis oculi, m. orbicularis oris, m. nasalis). This could lead to differences in cumulative train times between this study and the study performed by Prell et al.

5.3 Clinical implications

This study shows that a neural network can be trained to recognize patterns in EMG signals. The trained model can evaluate data in real-time and is therefore clinically applicable.

The trained model has high sensitivity and specificity in detecting A-trains and can be used in other research towards A-trains.

However, no correlation is found between A- train time and post-op HB score. Within our database the A-train time is not a predictor for post-op facial nerve damage. Based on our find-

ings the A-train should not be used for predicting facial nerve damage.

5.4 Study limitations

The number of the patients in this study that have fully annotated datasets is low (n=8). The number of patients that are partially annotated is 11. This includes patients with discarded anno- tations of certain channels due to hesitation for the signal to be an A-train or not, as described in 5.4. The number of segments used for training is however considered to be sufficient for training a neural network without overfitting to the selected segments.

The annotation process is done by hand, re- sulting in the fact that the accuracy of the anno- tations depend on the expertise of the annotator. Researcher bias has been limited by only having one single expert annotating the database. A sec- ond observant, an expert in the field of neurophys- iology, has been monitoring whether the annotator did the observations correctly and when in doubt a verbal motivation had been given and adjusted if necessary. The result of the discussion leads to the decision for the segment to be labeled to most certainly be an A-train, most certainly not be an A-train or to be entirely left out of the training data set because there is too much doubt to make a substantiated decision.

Leaving out the data that is too difficult to interpret, mostly due to multiple signals and rhythms firing simultaneously, may have influ- enced the outcome of this research. This deci- sion to leave exclude this data is made to create a dataset with segments of which we are confident they can be considered an A-train, instead of in- cluding debatable segments of data. The effect of including only the A-trains of which we are confi- dent on the total A-train time found in patients is unknown. It could potentially result in A-trains that are too distorted to visually score with con- fidence to not be detected by the model.

The most influencing pattern in the EMG that is not an A-train is the electrosurgery artefact. These artefacts are sometimes hard to differenti- ate from A-trains. A lot of effort is put into cor- rectly annotating the A-trains, but there is always a chance that a small percentage of electrosurgery

artefacts are mistaken to be an A-train.

The database consists of 38 patients, yet not all data of all patients is annotated. The sheer amount of time it would take to annotate more than just the A-train events, led to the decision to only annotate the A-trains since this is the pattern this research is aimed towards. A se- lection of patients has been annotated, and has been analyzed for diversity in A-train patterns. Upon visual inspection, the assumption is made that the annotated dataset contained a sufficient variety for training purposes. Inclusion of more annotated data may have lead to a more extensive database with segments containing an A-train and therefore more variation in the training dataset. However, we refrained from doing so due to the expected nominal improvement in sensitivity and specificity compared to the labour intensity of the annotation process.

As described in Section 3.4 and seen in Fig- ure 11 a threshold is set to reduce segments with incomplete A-trains. Because a sliding window technique without overlap is used, these incom- plete A-trains are not accounted for. This results in segments with short, incomplete segments of A- trains to be labeled to not have an A-train, reduc- ing the detected A-train time. This could result in a lower detected A-train time, but is not con- sidered to influence the outcome substantially. A sliding window technique with overlap could po- tentially be a better option, but could also intro- duce an overestimation of A-train time by count- ing part of an A-train twice, increasing the total A-train time.

5.4.1 Electrosurgery artefacts

The fact that electrosurgery artefacts are visually similar to A-trains when looked at in a 64ms seg- ment makes training a model substantially more difficult. In contrast to A-trains, the electro- surgery artefact is often visible in more than 1 channel simultaneously. In theory, every time the network detects an A-train in more than 1 channel at the same time, it can be disregarded because it is likely a electrosurgery artefact. However, there are patients in which A-trains are observed in dif- ferent channels simultaneously. This is most likely due to the small distance between the EMG nee-

dles of different channels. Likewise, there are cases in which the electrosurgery artefact is observed in just 1 channel.

An attempt has been made to offer all 4 to a network simultaneously, this did not yield a so- lution for the problem in our case. The network ended up discarding all A-trains in the first and second channel, partially detecting A-trains in the third channel and putting the emphasis on just the fourth channel. This could be due to the fact that the most A-trains in the annotated dataset are present in the third and fourth channel.

6

Conclusion

The model created for the purpose of this study has a high sensitivity and specificity for A-train detection in EMG data. Using neural networks is a promising technique in enabling real-time pat- tern detection in EMG data. The similarity be- tween the electrosurgery artefact and the A-train is the most confounding aspect of the recorded EMG data. An emphasis on electrosurgery arte- fact detection or prevention can potentially im- prove A-train detection. Our results show no cor- relation between the detected A-train times and post-operative House Brackmann scores. There- fore, total A-train time are not advised to be used as a predictor for post-op facial nerve damage. Further research is needed to find a predictor for post-op facial nerve damage. Furthermore, more research is needed towards the etiology of A-trains to understand their clinical relevance.

7

Future recommendations

7.1 Data collection

The database currently available consists of 38 pa- tients. In order to improve the network, more data could be annotated. Increasing the amount of an- notated A-trains could potentially further improve the performance of the network. Every new pa- tient that is added to the database can be used as new test data for the algorithm.

7.2 Network training

The data is currently presented to the network per EMG channel. Another way of presenting the data to the network is to offer 4 of the facial nerve EMG channels simultaneously. This can poten- tially lead to the network to increase its ability to distinguish between electrosurgery artefact and A- train. This increase has do to with the fact that an electrosurgery artefact is generally present in more than 1 EMG channel at the same time.

7.3 Artefact detection

The biggest issue with A-train detection is the similarity between A-trains and electrosurgery artefacts. The artefacts generated by the elec- trosurgery are in the same frequency range as A- trains and can therefore not be filtered out with a band-stop filter without filtering out A-trains. Detection of the artefacts can possibly be done by using one EMG input channel as an ”artefact de- tector”. In order to do this, a device generating a current when the electrosurgery is activated has to be developed. This generated current has to be within the limits of the EMG detecting hardware so that the hardware does not have to be altered to detect the electrosurgery activation.

One of the suggested methods is the placement of a coil around the cable from the generator to the foot pedal controlling the electrosurgery. This coil will be connected to an EMG input channel, so that any activity on this EMG channel rep- resents active electrosurgery. Another suggested method is a splitter between the connection of the foot pedal and the generator. One connection of the splitter will stay connected to the generator, the other connection will be plugged into an EMG channel. Any activity on the corresponding EMG

channel represents active electrosurgery. Contrary to the first method using the coil, this method may require more work to become approved and used in practice because a working connection is inter- rupted by a splitter. Whereas the first method does not interrupt the current electrical circuit.

7.4 Realtime implementation

The models created in this research can analyse 64ms of data in 4.1 ms using a XEON CPU and in 0.4ms using a TITAN V GPU. This means the model can analyse the data quicker than the time it takes to generate the data and is therefore appli- cable in realtime analysis. The delay of the anal- ysis is dependent on the delay of extracting the data from the source and offering it to the model for analysis. The current system does not allow for data extraction during measurement. Data extraction during measurement is a critical as- pect in a realtime implementation. The suggested subsequent step in realising realtime monitoring is exploring ways to allow data extraction during measurement. A suggested approach would be to contact the current supplier of the intraoperative monitoring hardware and software. In case the current supplier does not cooperate, different sup- pliers of intraoperative monitoring hardware and software can be approached. A possible imple- mentation of the model created in this research in future hardware and software is a potentially interesting addition to the next generation of in- traoperative monitoring devices.

References

1_{RJS Briggs, G Fabinyi, and AH Kaye. Current}

management of acoustic neuromas: review of surgical approaches and outcomes. Journal of clinical neuroscience, 7(6):521–526, 2000.

2_{TH Lanman, DE Brackmann, WE Hitselberger,}

and B Subin. Report of 190 consecutive cases of large acoustic tumors (vestibular schwan- noma) removed via the translabyrinthine ap- proach.Journal of neurosurgery, 90(4):617–623, 1999.

3_{M Samii and C Matthies. Management of 1000}

vestibular schwannomas (acoustic neuromas): surgical management and results with an em- phasis on complications and how to avoid them.

Neurosurgery, 40(1):11–23, 1997.

4_{L Albert and JL Rhoton. The cerebellopontine}

angle and posterior fossa cranial nerves by ret- rosigmoid approach. Neurosurgery, 47(S3):93– 129, 2000.

5_{MP Sindou.Practical handbook of neurosurgery.}

Springer, 2009.

6_{JM Ryzenman, ML Pensak, and JM Tew. Pa-}

tient perception of comorbid conditions after acoustic neuroma management: survey results from the acoustic neuroma association. The Laryngoscope, 114(5):814–820, 2004.

7_{J Prell, C Strauss, J Rachinger, A Alfieri,}

C Scheller, K Herfurth, and S Rampp. Fa- cial nerve palsy after vestibular schwannoma surgery: dynamic risk-stratification based on continuous emg-monitoring. Clinical Neuro- physiology, 125(2):415–421, 2014.

8_{H Hunt Batjer and CM Loftus. Textbook of neu-} rological surgery: principles and practice. Lip- pincott Williams & Wilkins, 2002.

9_{WH Slattery 3rd, DE Brackmann, and W Hit-}

selberger. Middle fossa approach for hear- ing preservation with acoustic neuromas. The American journal of otology, 18(5):596–601, 1997.

10_{M Samii and C Matthies. Management of 1000}

vestibular schwannomas (acoustic neuromas):

the facial nerve-preservation and restitution of function. Neurosurgery, 40(4):684–695, 1997.

11_{M Samii, V Gerganov, and A Samii. Improved}

preservation of hearing and facial nerve function in vestibular schwannoma surgery via the ret- rosigmoid approach in a series of 200 patients.

Journal of neurosurgery, 105(4):527–535, 2006.

12_{LD Lunsford, A Niranjan, JC Flickinger,}

A Maitz, and D Kondziolka. Radiosurgery of vestibular schwannomas: summary of experi- ence in 829 cases. Journal of neurosurgery, 102:195–199, 2005.

13_{D Kondziolka, LD Lunsford, MR McLaughlin,}

and JC Flickinger. Long-term outcomes after radiosurgery for acoustic neuromas. New Eng- land Journal of Medicine, 339(20):1426–1433, 1998.

14_{C Matthies and M Samii. Management of}

vestibular schwannomas (acoustic neuromas): the value of neurophysiology for intraoperative monitoring of auditory function in 200 cases.

Neurosurgery, 40(3):459–468, 1997.

15_{S Duarte-Costa, R Vaz, D Pinto, F Silveira,}

and journal=Acta neurochirurgica volume=157 number=11 pages=1991–1998 year=2015 pub- lisher=Springer Cerejo, A. Predictive value of intraoperative neurophysiologic monitoring in assessing long-term facial function in grade iv vestibular schwannoma removal.

16_{J Romst¨ock, C Strauss, and R Fahlbusch. Con-}

tinuous electromyography monitoring of mo- tor cranial nerves during cerebellopontine an- gle surgery.Journal of neurosurgery, 93(4):586– 593, 2000.

17_{J Prell, S Rampp, J Romst¨ock, R Fahlbusch,}

and C Strauss. Train time as a quantitative elec- tromyographic parameter for facial nerve func- tion in patients undergoing surgery for vestibu- lar schwannoma. Journal of neurosurgery, 106(5):826–832, 2007.

18_{A Vilamala, KH Madsen, and LK Hansen.}

Deep convolutional neural networks for inter- pretable analysis of eeg sleep stage scoring.

19_{L Cen, ZLg Yu, Y Tang, W Shi, T Kluge, and}

W Ser. Deep learning method for sleep stage classification. In International Conference on Neural Information Processing, pages 796–802. Springer, 2017.

20_{A Brenner, E Kutafina, and SM Jonas. Auto-}

matic recognition of epileptiform eeg abnormal- ities. Studies in health technology and informat- ics, 247:171–175, 2018.

21_{H Rajaguru and SK Prabhakar. Analysis of ad-}

aboost classifier from compressed eeg features for epilepsy detection. InComputing Methodolo- gies and Communication (ICCMC), 2017 Inter- national Conference on, pages 981–984. IEEE, 2017.

22_{Q Yuan, W Zhou, L Zhang, F Zhang, F Xu,}

Y Leng, D Wei, and M Chen. Epileptic seizure detection based on imbalanced classification and wavelet packet transform. Seizure, 50:99– 108, 2017.

23_{I S´anchez Fern´andez, AJ Sansevere, M Ga´ınza-}

Lein, K Kapur, and T Loddenkemper. Ma- chine learning for outcome prediction in elec- troencephalograph (eeg)-monitored children in the intensive care unit. Journal of child neurol- ogy, page 0883073818773230, 2018.