STQS:Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring

Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. In this paper, we propose a deep learning architecture for multi-modal sleep scoring, investigate the model's decision making process, and compare the model's reasoning with the annotation guidelines in the AASM manual. Our architecture, called STQS, uses convolutional neural networks (CNN) to automatically extract spatio-temporal features from 3 modalities (EEG, EOG and EMG), a bidirectional long short-term memory (Bi-LSTM) to extract sequential information, and residual connections to combine spatio-temporal and sequential features. We evaluated our model on two large datasets, obtaining an accuracy of 85% and 77% and a macro F1 score of 79% and 73% on SHHS and an in-house dataset, respectively. We further quantify the contribution of various architectural components and conclude that adding LSTM layers improves performance over a spatio-temporal CNN, while adding residual connections does not. Our interpretability results show that the output of the model is well aligned with AASM guidelines, and therefore, the model's decisions correspond to domain knowledge. We also compare multi-modal models and single-channel models and suggest that future research should focus on improving multi-modal models

Weakly Supervised Learning for Breast Cancer Prediction on Mammograms in Realistic Settings

Automatic methods for early detection of breast cancer on mammography can significantly decrease mortality. Broad uptake of those methods in hospitals is currently hindered because the methods have too many constraints. They assume annotations available for single images or even regions-of-interest (ROIs), and a fixed number of images per patient. Both assumptions do not hold in a general hospital setting. Relaxing those assumptions results in a weakly supervised learning setting, where labels are available per case, but not for individual images or ROIs. Not all images taken for a patient contain malignant regions and the malignant ROIs cover only a tiny part of an image, whereas most image regions represent benign tissue. In this work, we investigate a two-level multi-instance learning (MIL) approach for case-level breast cancer prediction on two public datasets (1.6k and 5k cases) and an in-house dataset of 21k cases. Observing that breast cancer is usually only present in one side, while images of both breasts are taken as a precaution, we propose a domain-specific MIL pooling variant. We show that two-level MIL can be applied in realistic clinical settings where only case labels, and a variable number of images per patient are available. Data in realistic settings scales with continuous patient intake, while manual annotation efforts do not. Hence, research should focus in particular on unsupervised ROI extraction, in order to improve breast cancer prediction for all patients.Comment: 10 pages, 5 figures, 5 table

From Anecdotal Evidence to Quantitative Evaluation Methods:A Systematic Review on Evaluating Explainable AI

The rising popularity of explainable artificial intelligence (XAI) to understand high-performing black boxes, also raised the question of how to evaluate explanations of machine learning (ML) models. While interpretability and explainability are often presented as a subjectively validated binary property, we consider it a multi-faceted concept. We identify 12 conceptual properties, such as Compactness and Correctness, that should be evaluated for comprehensively assessing the quality of an explanation. Our so-called Co-12 properties serve as categorization scheme for systematically reviewing the evaluation practice of more than 300 papers published in the last 7 years at major AI and ML conferences that introduce an XAI method. We find that 1 in 3 papers evaluate exclusively with anecdotal evidence, and 1 in 5 papers evaluate with users. We also contribute to the call for objective, quantifiable evaluation methods by presenting an extensive overview of quantitative XAI evaluation methods. This systematic collection of evaluation methods provides researchers and practitioners with concrete tools to thoroughly validate, benchmark and compare new and existing XAI methods. This also opens up opportunities to include quantitative metrics as optimization criteria during model training in order to optimize for accuracy and interpretability simultaneously.Comment: Link to website added: https://utwente-dmb.github.io/xai-papers

Energy dependence of ϕ meson production at forward rapidity in pp collisions at the LHC

The production of $\phi$ mesons has been studied in pp collisions at LHC energies with the ALICE detector via the dimuon decay channel in the rapidity region $2.5< y < 4$. Measurements of the differential cross section $\mathrm{d}^2\sigma /\mathrm{d}y \mathrm{d}p_{\mathrm {T}}$ are presented as a function of the transverse momentum ($p_{\mathrm {T}}$) at the center-of-mass energies $\sqrt{s}=5.02$, 8 and 13 TeV and compared with the ALICE results at midrapidity. The differential cross sections at $\sqrt{s}=5.02$ and 13 TeV are also studied in several rapidity intervals as a function of $p_{\mathrm {T}}$, and as a function of rapidity in three $p_{\mathrm {T}}$ intervals. A hardening of the $p_{\mathrm {T}}$-differential cross section with the collision energy is observed, while, for a given energy, $p_{\mathrm {T}}$ spectra soften with increasing rapidity and, conversely, rapidity distributions get slightly narrower at increasing $p_{\mathrm {T}}$. The new results, complementing the published measurements at $\sqrt{s}=2.76$ and 7 TeV, allow one to establish the energy dependence of $\phi$ meson production and to compare the measured cross sections with phenomenological models. None of the considered models manages to describe the evolution of the cross section with $p_{\mathrm {T}}$ and rapidity at all the energies.publishedVersio

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Multiplicity dependence of (anti-)deuteron production in pp collisions at root s=7 TeV

none1019siIn this letter, the production of deuterons and anti-deuterons in pp collisions at root s = 7 TeV is studied as a function of the charged-particle multiplicity density at mid-rapidity with the ALICE detector at the LHC. Production yields are measured at mid-rapidity in five multiplicity classes and as a function of the deuteron transverse momentum (p(T)). The measurements are discussed in the context of hadron-coalescence models. The coalescence parameter B-2, extracted from the measured spectra of (anti-)deuteronsand primary (anti-)protons, exhibits no significant p(T)-dependence for p(T) < 3 GeV/c, in agreement with the expectations of a simple coalescence picture. At fixed transverse momentum per nucleon, the B-2 parameter is found to decrease smoothly from low multiplicity pp to Pb-Pb collisions, in qualitative agreement with more elaborate coalescence models. The measured mean transverse momentum of (anti-)deuterons in pp is not reproduced by the Blast-Wave model calculations that simultaneously describe pion, kaon and proton spectra, in contrast to central Pb-Pb collisions. The ratio between the p(T)-integrated yield of deuterons to protons, d/p, is found to increase with the charged-particle multiplicity, as observed in inelastic pp collisions at different centre-of-mass energies. The d/p ratios are reported in a wide range, from the lowest to the highest multiplicity values measured in pp collisions at the LHC. (C) 2019 The Author(s). Published by Elsevier B.VnoneAcharya, S.; Acosta, F. T.; Adamova, D.; Adhya, S. P.; Adler, A.; Adolfsson, J.; Aggarwal, M. M.; Rinella, G. Aglieri; Agnello, M.; Ahammed, Z.; Ahmad, S.; Ahn, S. U.; Aiola, S.; Akindinov, A.; Al-Turany, M.; Alam, S. N.; Albuquerque, D. S. D.; Aleksandrov, D.; Alessandro, B.; Alfanda, H. M.; Alfaro Molina, R.; Ali, B.; Ali, Y.; Alici, A.; Alkin, A.; Alme, J.; Alt, T.; Altenkamper, L.; Altsybeev, I; Anaam, M. N.; Andrei, C.; Andreou, D.; Andrews, H. A.; Andronic, A.; Angeletti, M.; Anguelov, V; Anson, C.; Anticic, T.; Antinori, F.; Antonioli, P.; Anwar, R.; Apadula, N.; Aphecetche, L.; Appelshaeuser, H.; Arcelli, S.; Arnaldi, R.; Arratia, M.; Arsene, I. C.; Arslandok, M.; Augustinus, A.; Averbeck, R.; Azmi, M. D.; Badala, A.; Baek, Y. W.; Bagnasco, S.; Bailhache, R.; Bala, R.; Baldisseri, A.; Ball, M.; Baral, R. C.; Barbera, R.; Barioglio, L.; Barnafoldi, G. G.; Barnby, L. S.; Barret, V; Bartalini, P.; Barth, K.; Bartsch, E.; Bastid, N.; Basu, S.; Batigne, G.; Batyunya, B.; Batzing, P. C.; Bauri, D.; Bazo Alba, J. L.; Bearden, I. G.; Bedda, C.; Behera, N. K.; Belikov, I; Bellini, F.; Bello Martinez, H.; Bellwied, R.; Beltran, L. G. E.; Belyaev, V; Bencedi, G.; Beole, S.; Bercuci, A.; Berdnikov, Y.; Berenyi, D.; Bertens, R. A.; Berzano, D.; Betev, L.; Bhasin, A.; Bhat, I. R.; Bhatt, H.; Bhattacharjee, B.; Bianchi, A.; Bianchi, L.; Bianchi, N.; Bielcik, J.; Bielcikova, J.; Bilandzic, A.; Biro, G.; Biswas, R.; Biswas, S.; Blair, J. T.; Blau, D.; Blume, C.; Boca, G.; Bock, F.; Bogdanov, A.; Boldizsar, L.; Bolozdynya, A.; Bombara, M.; Bonomi, G.; Bonora, M.; Borel, H.; Borissov, A.; Borri, M.; Botta, E.; Bourjau, C.; Bratrud, L.; Braun-Munzinger, P.; Bregant, M.; Broker, T. A.; Broz, M.; Brucken, E. J.; Bruna, E.; Bruno, G. E.; Buckland, M. D.; Budnikov, D.; Buesching, H.; Bufalino, S.; Buhler, P.; Buncic, P.; Busch, O.; Buthelezi, Z.; Butt, J. B.; Buxton, J. T.; Caffarri, D.; Caines, H.; Caliva, A.; Calvo Villar, E.; Camacho, R. S.; Camerini, P.; Capon, A. A.; Carnesecchi, F.; Castellanos, J. Castillo; Castro, A. J.; Casula, E. A. R.; Sanchez, C. Ceballos; Chakraborty, P.; Chandra, S.; Chang, B.; Chang, W.; Chapeland, S.; Chartier, M.; Chattopadhyay, S.; Chauvin, A.; Cheshkov, C.; Cheynis, B.; Barroso, V. Chibante; Chinellato, D. D.; Cho, S.; Chochula, P.; Chowdhury, T.; Christakoglou, P.; Christensen, C. H.; Christiansen, P.; Chujo, T.; Cicalo, C.; Cifarelli, L.; Cindolo, F.; Cleymans, J.; Colamaria, F.; Colella, D.; Collu, A.; Colocci, M.; Concas, M.; Balbastre, G. Conesa; del Valle, Z. Conesa; Contin, G.; Contreras, J. G.; Cormier, T. M.; Morales, Y. Corrales; Cortese, P.; Cosentino, M. R.; Costa, F.; Costanza, S.; Crkovska, J.; Crochet, P.; Cuautle, E.; Cunqueiro, L.; Dabrowski, D.; Dahms, T.; Dainese, A.; Damas, F. P. A.; Dani, S.; Danisch, M. C.; Danu, A.; Das, D.; Das, I; Das, S.; Dash, A.; Dash, S.; Dashi, A.; De, S.; De Caro, A.; de Cataldo, G.; de Conti, C.; de Cuveland, J.; De Falco, A.; De Gruttola, D.; De Marco, N.; De Pasquale, S.; De Souza, R. D.; Degenhardt, H. F.; Deisting, A.; Deloff, A.; Delsanto, S.; Dhankher, P.; Di Bari, D.; Di Mauro, A.; Diaz, R. A.; Dietel, T.; Dillenseger, P.; Ding, Y.; Divia, R.; Djuvsland, O.; Dobrin, A.; Domenicis Gimenez, D.; Doenigus, B.; Dordic, O.; Dubey, A. K.; Dubla, A.; Dudi, S.; Duggal, A. K.; Dukhishyam, M.; Dupieux, P.; Ehlers, R. J.; Elia, D.; Engel, H.; Epple, E.; Erazmus, B.; Erhardt, F.; Erokhin, A.; Ersdal, M. R.; Espagnon, B.; Eulisse, G.; Eum, J.; Evans, D.; Evdokimov, S.; Fabbietti, L.; Faggin, M.; Faivre, J.; Fantoni, A.; Fasel, M.; Feldkamp, L.; Feliciello, A.; Feofilov, G.; Fernandez Tellez, A.; Ferrero, A.; Ferretti, A.; Festanti, A.; Feuillard, V. J. G.; Figiel, J.; Filchagin, S.; Finogeev, D.; Fionda, F. M.; Fiorenza, G.; Flor, F.; Floris, M.; Foertsch, S.; Foka, P.; Fokin, S.; Fragiacomo, E.; Francisco, A.; Frankenfeld, U.; Fronze, G. G.; Fuchs, U.; Furget, C.; Furs, A.; Girard, M. Fusco; Gaardhoje, J. J.; Gagliardi, M.; Gago, A. M.; Gajdosova, K.; Galvan, C. D.; Ganoti, P.; Garabatos, C.; Garcia-Solis, E.; Garg, K.; Gargiulo, C.; Garner, K.; Gasik, P.; Gauger, E. F.; Gay Ducati, M. B.; Germain, M.; Ghosh, J.; Ghosh, P.; Ghosh, S. K.; Gianotti, P.; Giubellino, P.; Giubilato, P.; Glaessel, P.; Gomez Coral, D. M.; Ramirez, A. Gomez; Gonzalez, V; Gonzalez-Zamora, P.; Gorbunov, S.; Gorlich, L.; Gotovac, S.; Grabski, V; Graczykowski, L. K.; Graham, K. L.; Greiner, L.; Grelli, A.; Grigoras, C.; Grigoriev, V; Grigoryan, A.; Grigoryan, S.; Gronefeld, J. M.; Grosa, F.; Grosse-Oetringhaus, J. F.; Grosso, R.; Guernane, R.; Guerzoni, B.; Guittiere, M.; Gulbrandsen, K.; Gunji, T.; Gupta, A.; Gupta, R.; Guzman, I. B.; Haake, R.; Habib, M. K.; Hadjidakis, C.; Hamagaki, H.; Hamar, G.; Hamid, M.; Hamon, J. C.; Hannigan, R.; Haque, M. R.; Harlenderova, A.; Harris, J. W.; Harton, A.; Hassan, H.; Hatzifotiadou, D.; Hauer, P.; Hayashi, S.; Heckel, S. T.; Hellbaer, E.; Helstrup, H.; Herghelegiu, A.; Hernandez, E. G.; Herrera Corral, G.; Herrmann, F.; Hetland, K. F.; Hilden, T. E.; Hillemanns, H.; Hills, C.; Hippolyte, B.; Hohlweger, B.; Horak, D.; Hornung, S.; Hosokawa, R.; Hota, J.; Hristov, P.; Huang, C.; Hughes, C.; Huhn, P.; Humanic, T. J.; Hushnud, H.; Husova, L. A.; Hussain, N.; Hussain, T.; Hutter, D.; Hwang, D. S.; Iddon, J. P.; Ilkaev, R.; Inaba, M.; Ippolitov, M.; Islam, M. S.; Ivanov, M.; Ivanov, V; Izucheev, V; Jacak, B.; Jacazio, N.; Jacobs, P. M.; Jadhav, M. B.; Jadlovska, S.; Jadlovsky, J.; Jaelani, S.; Jahnke, C.; Jakubowska, M. J.; Janik, M. A.; Jercic, M.; Jevons, O.; Bustamante, R. T. Jimenez; Jin, M.; Jones, P. G.; Jusko, A.; Kalinak, P.; Kalweit, A.; Kang, J. H.; Kaplin, V; Kar, S.; Uysal, A. Karasu; Karavichev, O.; Karavicheva, T.; Karczmarczyk, P.; Karpechev, E.; Kebschull, U.; Keidel, R.; Keil, M.; Ketzer, B.; Khabanova, Z.; Khan, A. M.; Khan, S.; Khan, S. A.; Khanzadeev, A.; Kharlov, Y.; Khatun, A.; Khuntia, A.; Kielbowicz, M. M.; Kileng, B.; Kim, B.; Kim, D.; Kim, D. J.; Kim, E. J.; Kim, H.; Kim, J. S.; Kim, J.; Kim, M.; Kim, S.; Kim, T.; Kindra, K.; Kirsch, S.; Kisel, I; Kiselev, S.; Kisiel, A.; Klay, J. L.; Klein, C.; Klein, J.; Klein, S.; Klein-Boesing, C.; Klewin, S.; Kluge, A.; Knichel, M. L.; Knospe, A. G.; Kobdaj, C.; Kofarago, M.; Koehler, M. K.; Kollegger, T.; Kondratyeva, N.; Kondratyuk, E.; Konopka, P. J.; Konyushikhin, M.; Koska, L.; Kovalenko, O.; Kovalenko, V; Kowalski, M.; Kralik, I; Kravcakova, A.; Kreis, L.; Krivda, M.; Krizek, F.; Krueger, M.; Kryshen, E.; Krzewicki, M.; Kubera, A. M.; Kucera, V; Kuhn, C.; Kuijer, P. G.; Kumar, L.; Kumar, S.; Kundu, S.; Kurashvili, P.; Kurepin, A.; Kurepin, A. B.; Kushpil, S.; Kvapil, J.; Kweon, M. J.; Kwon, Y.; La Pointe, S. L.; La Rocca, P.; Lai, Y. S.; Langoy, R.; Lapidus, K.; Lardeux, A.; Larionov, P.; Laudi, E.; Lavicka, R.; Lazareva, T.; Lea, R.; Leardini, L.; Lee, S.; Lehas, F.; Lehner, S.; Lehrbach, J.; Lemmon, R. C.; Leon Monzon, I; Levai, P.; Li, X.; Li, X. L.; Lien, J.; Lietava, R.; Lim, B.; Lindal, S.; Lindenstruth, V; Lindsay, S. W.; Lippmann, C.; Lisa, M. A.; Litichevskyi, V; Liu, A.; Ljunggren, H. M.; Llope, W. J.; Lodato, D. F.; Loginov, V; Loizides, C.; Loncar, P.; Lopez, X.; Lopez Torres, E.; Luettig, P.; Luhder, J. R.; Lunardon, M.; Luparello, G.; Lupi, M.; Maevskaya, A.; Mager, M.; Mahmood, S. M.; Mahmoud, T.; Maire, A.; Majka, R. D.; Malaev, M.; Malik, Q. W.; Malinina, L.; Mal'Kevich, D.; Malzacher, P.; Mamonov, A.; Manko, V; Manso, F.; Manzari, V; Mao, Y.; Marchisone, M.; Mares, J.; Margagliotti, G., V; Margotti, A.; Margutti, J.; Marin, A.; Markert, C.; Marquard, M.; Martin, N. A.; Martinengo, P.; Martinez, J. L.; Martinez, M., I; Garcia, G. Martinez; Pedreira, M. Martinez; Masciocchi, S.; Masera, M.; Masoni, A.; Massacrier, L.; Masson, E.; Mastroserio, A.; Mathis, A. M.; Matuoka, P. F. T.; Matyja, A.; Mayer, C.; Mazzilli, M.; Mazzoni, M. A.; Meddi, F.; Melikyan, Y.; Menchaca-Rocha, A.; Meninno, E.; Meres, M.; Mhlanga, S.; Miake, Y.; Micheletti, L.; Mieskolainen, M. M.; Mihaylov, D. L.; Mikhaylov, K.; Mischke, A.; Mishra, A. N.; Miskowiec, D.; Mitra, J.; Mitu, C. M.; Mohammadi, N.; Mohanty, A. P.; Mohanty, B.; Khan, M. Mohisin; Mondal, M. M.; Mordasini, C.; De Godoy, D. A. Moreira; Moreno, L. A. P.; Moretto, S.; Morreale, A.; Morsch, A.; Mrnjavac, T.; Muccifora, V; Mudnic, E.; Muehlheim, D.; Muhuri, S.; Mukherjee, M.; Mulligan, J. D.; Munhoz, M. G.; Muenning, K.; Munzer, R. H.; Murakami, H.; Murray, S.; Musa, L.; Musinsky, J.; Myers, C. J.; Myrcha, J. W.; Naik, B.; Nair, R.; Nandi, B. K.; Nania, R.; Nappi, E.; Naru, M. U.; Nassirpour, A. F.; Natal da Luz, H.; Nattrass, C.; Navarro, S. R.; Nayak, K.; Nayak, R.; Nayak, T. K.; Nazarenko, S.; De Oliveira, R. A. Negrao; Nellen, L.; Nesbo, S., V; Neskovic, G.; Ng, F.; Nielsen, B. S.; Nikolaev, S.; Nikulin, S.; Nikulin, V; Noferini, F.; Nomokonov, P.; Nooren, G.; Noris, J. C. C.; Norman, J.; Nyanin, A.; Nystrand, J.; Ogino, M.; Ohlson, A.; Oleniacz, J.; Oliveira Da Silva, A. C.; Oliver, M. H.; Onderwaater, J.; Oppedisano, C.; Orava, R.; Ortiz Velasquez, A.; Oskarsson, A.; Otwinowski, J.; Oyama, K.; Pachmayer, Y.; Pacik, V; Pagano, D.; Paic, G.; Palni, P.; Pan, J.; Pandey, A. K.; Panebianco, S.; Papikyan, V; Pareek, P.; Park, J.; Parkkila, J. E.; Parmar, S.; Passfeld, A.; Pathak, S. P.; Patra, R. N.; Paul, B.; Pei, H.; Peitzmann, T.; Peng, X.; Pereira, L. G.; Da Costa, H. Pereira; Peresunko, D.; Perez, G. M.; Lezama, E. Perez; Peskov, V; Pestov, Y.; Petracek, V; Petrovici, M.; Pezzi, R. P.; Piano, S.; Pikna, M.; Pillot, P.; Pimentel, L. O. D. L.; Pinazza, O.; Pinsky, L.; Pisano, S.; Piyarathna, D. B.; Ploskon, M.; Planinic, M.; Pliquett, F.; Pluta, J.; Pochybova, S.; Podesta-Lerma, P. L. M.; Poghosyan, M. G.; Polichtchouk, B.; Poljak, N.; Poonsawat, W.; Pop, A.; Poppenborg, H.; Porteboeuf-Houssais, S.; Pozdniakov, V; Prasad, S. K.; Preghenella, R.; Prino, F.; Pruneau, C. A.; Pshenichnov, I; Puccio, M.; Punin, V; Puranapanda, K.; Putschke, J.; Quishpe, R. E.; Ragoni, S.; Raha, S.; Rajput, S.; Rak, J.; Rakotozafindrabe, A.; Ramello, L.; Rami, F.; Raniwala, R.; Raniwala, S.; Rasanen, S. S.; Rascanu, B. T.; Rath, R.; Ratza, V; Ravasenga, I; Read, K. F.; Redlich, K.; Rehman, A.; Reichelt, P.; Reidt, F.; Ren, X.; Renfordt, R.; Reshetin, A.; Revol, J-P; Reygers, K.; Riabov, V; Richert, T.; Richter, M.; Riedler, P.; Riegler, W.; Riggi, F.; Ristea, C.; Rode, S. P.; Rodriguez Cahuantzi, M.; Roed, K.; Rogalev, R.; Rogochaya, E.; Rohr, D.; Rohrich, D.; Rokita, P. S.; Ronchetti, F.; Rosas, E. D.; Roslon, K.; Rosnet, P.; Rossi, A.; Rotondi, A.; Roukoutakis, F.; Roy, A.; Roy, P.; Rueda, O., V; Rui, R.; Rumyantsev, B.; Rustamov, A.; Ryabinkin, E.; Ryabov, Y.; Rybicki, A.; Saarinen, S.; Sadhu, S.; Sadovsky, S.; Safarik, K.; Saha, S. K.; Sahoo, B.; Sahoo, P.; Sahoo, R.; Sahoo, S.; Sahu, P. K.; Saini, J.; Sakai, S.; Sambyal, S.; Samsonov, V; Sandoval, A.; Sarkar, A.; Sarkar, D.; Sarkar, N.; Sarma, P.; Sarti, V. M.; Sas, M. H. P.; Scapparone, E.; Schaefer, B.; Schambach, J.; Scheid, H. S.; Schiaua, C.; Schicker, R.; Schmah, A.; Schmidt, C.; Schmidt, H. R.; Schmidt, M. O.; Schmidt, M.; Schmidt, N., V; Schmier, A. R.; Schukraft, J.; Schutz, Y.; Schwarz, K.; Schweda, K.; Scioli, G.; Scomparin, E.; Sefcik, M.; Seger, J. E.; Sekiguchi, Y.; Sekihata, D.; Selyuzhenkov, I; Senyukov, S.; Serradilla, E.; Sett, P.; Sevcenco, A.; Shabanov, A.; Shabetai, A.; Shahoyan, R.; Shaikh, W.; Shangaraev, A.; Sharma, A.; Sharma, M.; Sharma, N.; Sheikh, A., I; Shigaki, K.; Shimomura, M.; Shirinkin, S.; Shou, Q.; Sibiriak, Y.; Siddhanta, S.; Siemiarczuk, T.; Silvermyr, D.; Simatovic, G.; Simonetti, G.; Singh, R.; Singhal, V; Sinha, T.; Sitar, B.; Sitta, M.; Skaali, T. B.; Slupecki, M.; Smirnov, N.; Snellings, R. J. M.; Snellman, T. 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Channel Contribution In Deep Learning Based Automatic Sleep Scoring – How Many Channels Do We Need?

Machine learning based sleep scoring methods aim to automate the process of annotating polysomnograms with sleep stages. Although sleep signals of multiple modalities and channels should contain more information according to sleep guidelines, most multi-channel multi modal models in the literature showed only a little performance improvement compared to single-channel EEG models and sometimes even failed to outperform them. In this paper, we investigate whether the high performance of single-channel EEG models can be attributed to specific model features in their deep learning architectures and to which extent multi-channel multi-modal models take the information from different channels of modalities into account. First, we transfer the model features from single channel EEG models, such as combinations of small and large filters in CNNs, to multi-channel multi-modal models and measure their impacts. Second, we employ two explainability methods, the layer-wise relevance propagation as post-hoc and the embedded channel attention network as intrinsic interpretability methods, to measure the contribution of different channels on predictive performance. We find that i) single-channel model features can improve the performance of multi-channel multi-modal models and ii) multi-channel multi-modal models focus on one important channel per modality and use the remaining channels to complement the information of the focused channels. Our results suggest that more advanced methods for aggregating channel information using complementary information from other channels may improve sleep scoring performance for multi-channel multi-modal models